GGPLOT_2024
Ravi Hiremath
2024-01-16
R Markdown
A <- c(2,1,4,9)
B <- c(3,2,5,12)
C <- c(4,1,14,8)
D <- c("a","a","b","b")
##create a data frame #
mydata <- data.frame(A,B,C,D)
mydata## A B C D
## 1 2 3 4 a
## 2 1 2 1 a
## 3 4 5 14 b
## 4 9 12 8 blibrary(ggplot2)
p <- ggplot(
data=mydata,
mapping=aes(x=A,y=C)
)
p
##adding layers/one of the geometries
##scatter plot=geom_point
p <- p+geom_point(
mapping = aes(
shape=D
), stat = "identity", size=4, position = "identity" ## meaning not going to do anything with the position, "identity" is also by default
)
p
#adding a scale
p <- p+scale_x_continuous()+scale_y_log10()
p
# add onr coordinate system/by default its a cartesian coordinate
p <- p+coord_cartesian()
p
#facet specifcation/ this whole Just for example to illustrate the GoG
p <- p+facet_grid(cols=vars(D))
p
##above in simple code
# p2 <- ggplot(mydata,aes(x=A,y=C))+geom_point(aes(shape=(D), size=4)+scale_y_log10()+ facet_grid(cols=vars(D))
# p2#Geometries=Geoms-control the type of plot you create
library(ggplot2)
p <- ggplot(data=mpg,mapping = aes(x=displ, y=hwy))
p
p+geom_point()
p+geom_line()
p+geom_line()+geom_point()
p+geom_path()
#p+geom_bar()
p <- ggplot(data=mpg,mapping = aes(x=displ))+geom_bar()
p
p <- ggplot(data=mpg,mapping = aes(x=displ, y=hwy))+geom_point(shape=8,size=4)
p
p <- ggplot(data=mpg,mapping = aes(x=displ))+geom_bar(fill="red",shape=8)## Warning in geom_bar(fill = "red", shape = 8): Ignoring unknown parameters:
## `shape`p
p <- ggplot(data=mpg,mapping = aes(x=displ, y=hwy))+geom_point()+labs(title="Plot title"
,x="Displacement",y="Highway")
p
p <- ggplot(data=mpg,mapping = aes(x=displ, y=hwy))+geom_point()+labs(title="Plot title" ,x="Displacement",y="Highway")+theme(text=element_text(size=15))
p
##Line plots #dataset=babynames
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(babynames)
babynames## # A tibble: 1,924,665 × 5
## year sex name n prop
## <dbl> <chr> <chr> <int> <dbl>
## 1 1880 F Mary 7065 0.0724
## 2 1880 F Anna 2604 0.0267
## 3 1880 F Emma 2003 0.0205
## 4 1880 F Elizabeth 1939 0.0199
## 5 1880 F Minnie 1746 0.0179
## 6 1880 F Margaret 1578 0.0162
## 7 1880 F Ida 1472 0.0151
## 8 1880 F Alice 1414 0.0145
## 9 1880 F Bertha 1320 0.0135
## 10 1880 F Sarah 1288 0.0132
## # ℹ 1,924,655 more rowsanna <- filter(babynames,name=="Anna", sex=="F")
anna## # A tibble: 138 × 5
## year sex name n prop
## <dbl> <chr> <chr> <int> <dbl>
## 1 1880 F Anna 2604 0.0267
## 2 1881 F Anna 2698 0.0273
## 3 1882 F Anna 3143 0.0272
## 4 1883 F Anna 3306 0.0275
## 5 1884 F Anna 3860 0.0281
## 6 1885 F Anna 3994 0.0281
## 7 1886 F Anna 4283 0.0279
## 8 1887 F Anna 4227 0.0272
## 9 1888 F Anna 4982 0.0263
## 10 1889 F Anna 5062 0.0268
## # ℹ 128 more rows#default theme set up
theme_set(theme_minimal())
ggplot(anna,aes(x=year,y=n))+geom_line()
ggplot(anna,aes(x=year,y=n))+geom_step()#useful to see where exactly the change occured
ggplot(anna,aes(x=year,y=n))+geom_line(size=1.2)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
ggplot(anna,aes(x=year,y=n))+geom_line(size=1.2,alpha=0.5)
ggplot(anna,aes(x=year,y=n))+geom_line(size=1.2,alpha=0.5,linetype="dashed")
ggplot(anna,aes(x=year,y=n))+geom_line(size=1.2,color="midnightblue")
ggplot(anna,aes(x=year,y=n))+geom_line(size=1.2,color="#E7B721")
#colors() to check all colors available#install.packages("viridis")# for different Palette
library("viridis")## Warning: package 'viridis' was built under R version 4.3.2## Loading required package: viridisLitebaby <- filter(babynames, name %in% c("Anna","Emma","Clara"),sex=="F")
baby## # A tibble: 414 × 5
## year sex name n prop
## <dbl> <chr> <chr> <int> <dbl>
## 1 1880 F Anna 2604 0.0267
## 2 1880 F Emma 2003 0.0205
## 3 1880 F Clara 1226 0.0126
## 4 1881 F Anna 2698 0.0273
## 5 1881 F Emma 2034 0.0206
## 6 1881 F Clara 1242 0.0126
## 7 1882 F Anna 3143 0.0272
## 8 1882 F Emma 2303 0.0199
## 9 1882 F Clara 1490 0.0129
## 10 1883 F Anna 3306 0.0275
## # ℹ 404 more rows## Not a good plot
ggplot(baby,aes(x=year,y=n))+geom_line()
# Good plot
ggplot(baby,aes(x=year,y=n,group=name))+geom_line()
ggplot(baby,aes(x=year,y=n,group=name))+geom_line()+scale_color_viridis()# nothing happens
library(viridis)
# ggplot(baby,aes(x=year,y=n,group=name,color=name))+geom_line()+scale_color_viridis() #Gives erro because 'n' is continuous#use beloe code in the terminals to export it to image/pdf
ggplot(baby,aes(x=year,y=n,group=name,color=name))+geom_line()+scale_color_viridis(discrete = T)# Name is discrete
babyrRange <-filter(baby,year>1980 & year<max(baby$year))
babyrRange## # A tibble: 108 × 5
## year sex name n prop
## <dbl> <chr> <chr> <int> <dbl>
## 1 1981 F Anna 5191 0.00290
## 2 1981 F Emma 534 0.000299
## 3 1981 F Clara 320 0.000179
## 4 1982 F Anna 5270 0.00291
## 5 1982 F Emma 562 0.000310
## 6 1982 F Clara 326 0.000180
## 7 1983 F Anna 5218 0.00292
## 8 1983 F Emma 551 0.000308
## 9 1983 F Clara 310 0.000173
## 10 1984 F Anna 5305 0.00294
## # ℹ 98 more rowsggplot(babyrRange,aes(x=year,y=n,group=name,color=name))+geom_line()
ggplot(babyrRange,aes(x=year,y=n,group=name,color=name))+geom_line()+scale_color_viridis(discrete = T)+geom_point()
babyrRange## # A tibble: 108 × 5
## year sex name n prop
## <dbl> <chr> <chr> <int> <dbl>
## 1 1981 F Anna 5191 0.00290
## 2 1981 F Emma 534 0.000299
## 3 1981 F Clara 320 0.000179
## 4 1982 F Anna 5270 0.00291
## 5 1982 F Emma 562 0.000310
## 6 1982 F Clara 326 0.000180
## 7 1983 F Anna 5218 0.00292
## 8 1983 F Emma 551 0.000308
## 9 1983 F Clara 310 0.000173
## 10 1984 F Anna 5305 0.00294
## # ℹ 98 more rowsggplot(babyrRange,aes(x=year,y=n,group=name,color=name))+geom_line(linetype="dashed")+scale_color_viridis(discrete = T)+geom_point()
babyrRange## # A tibble: 108 × 5
## year sex name n prop
## <dbl> <chr> <chr> <int> <dbl>
## 1 1981 F Anna 5191 0.00290
## 2 1981 F Emma 534 0.000299
## 3 1981 F Clara 320 0.000179
## 4 1982 F Anna 5270 0.00291
## 5 1982 F Emma 562 0.000310
## 6 1982 F Clara 326 0.000180
## 7 1983 F Anna 5218 0.00292
## 8 1983 F Emma 551 0.000308
## 9 1983 F Clara 310 0.000173
## 10 1984 F Anna 5305 0.00294
## # ℹ 98 more rowsggplot(babyrRange,aes(x=year,y=n,group=name,color=name))+geom_line(linetype="dashed")+scale_color_viridis(discrete = T)+geom_point(size=.6, alpha=.5)
babyrRange## # A tibble: 108 × 5
## year sex name n prop
## <dbl> <chr> <chr> <int> <dbl>
## 1 1981 F Anna 5191 0.00290
## 2 1981 F Emma 534 0.000299
## 3 1981 F Clara 320 0.000179
## 4 1982 F Anna 5270 0.00291
## 5 1982 F Emma 562 0.000310
## 6 1982 F Clara 326 0.000180
## 7 1983 F Anna 5218 0.00292
## 8 1983 F Emma 551 0.000308
## 9 1983 F Clara 310 0.000173
## 10 1984 F Anna 5305 0.00294
## # ℹ 98 more rows
```r
#add/remove/override Aesthetics
library(ggplot2)
ggplot(data=mpg,aes(x=displ,y=hwy,color=hwy))+geom_point()
ggplot(data=mpg,aes(x=displ,y=hwy,color=hwy))+geom_point(aes(size=hwy))
ggplot(data=mpg,aes(x=displ,y=hwy,color=hwy))+geom_point(aes(x=class))#override x displ to class
ggplot(data=mpg,aes(x=displ,y=hwy,color=hwy))+geom_point(aes(x=class))+geom_line()#override x displ to class but add another geometry it gives x=displ as well
ggplot(data=mpg,aes(x=displ,y=hwy,color=hwy))+geom_point(aes(color=NULL))##overrding the color
##mapping vs Settings
ggplot(mpg,aes(x=displ,y=hwy))+geom_point(aes(color=hwy, sixe=3))#aestetice## Warning in geom_point(aes(color = hwy, sixe = 3)): Ignoring unknown aesthetics:
## sixe
ggplot(mpg,aes(x=displ,y=hwy))+geom_point(color="darkblue", size=3)#setting/set-single color
#The current dataset=longWidedata is not in good shape to plot as it is wide
#library(ggplot2)
#dd <- longWidedata
#dd
#ggplot(dd,aes(x=Year,color=Town))+geom_line(aes(y=Males))+geom_line(aes(y=Females), linetype="dashed")#The above dataset/ggplot works only when we have 2 columns or wideformat---not a good practice
##Hence change data from wide format to long format, reshape data by 'melt'
#library(reshape2)
#head(dd)
#melted_data <- melt(dd,id.vars = c("Town","Year"))#both males & females are measurement variables
#melted_data#line type should follow variable
#ggplot(melted_data, aes(x=Year,y=value, group=interaction(variable,Town), color=Town, linetype=variable))+geom_line()+geom_point()# More example on melt function
library(reshape2)
head(french_fries,3)## time treatment subject rep potato buttery grassy rancid painty
## 61 1 1 3 1 2.9 0.0 0 0.0 5.5
## 25 1 1 3 2 14.0 0.0 0 1.1 0.0
## 62 1 1 10 1 11.0 6.4 0 0.0 0.0frymelt <- melt(french_fries,id.vars = c("time","treatment","subject","rep")) # these are not measurement variables
frymelt #check head(french_fries) vs head (frymelt) to see the difference## time treatment subject rep variable value
## 1 1 1 3 1 potato 2.9
## 2 1 1 3 2 potato 14.0
## 3 1 1 10 1 potato 11.0
## 4 1 1 10 2 potato 9.9
## 5 1 1 15 1 potato 1.2
## 6 1 1 15 2 potato 8.8
## 7 1 1 16 1 potato 9.0
## 8 1 1 16 2 potato 8.2
## 9 1 1 19 1 potato 7.0
## 10 1 1 19 2 potato 13.0
## 11 1 1 31 1 potato 12.2
## 12 1 1 31 2 potato 12.8
## 13 1 1 51 1 potato 8.6
## 14 1 1 51 2 potato 10.2
## 15 1 1 52 1 potato 5.8
## 16 1 1 52 2 potato 7.0
## 17 1 1 63 1 potato 8.3
## 18 1 1 63 2 potato 2.9
## 19 1 1 78 1 potato 4.9
## 20 1 1 78 2 potato 8.8
## 21 1 1 79 1 potato 5.1
## 22 1 1 79 2 potato 10.4
## 23 1 1 86 1 potato 5.2
## 24 1 1 86 2 potato 3.0
## 25 1 2 3 1 potato 13.9
## 26 1 2 3 2 potato 13.4
## 27 1 2 10 1 potato 9.3
## 28 1 2 10 2 potato 11.0
## 29 1 2 15 1 potato 9.0
## 30 1 2 15 2 potato 7.0
## 31 1 2 16 1 potato 4.6
## 32 1 2 16 2 potato 5.0
## 33 1 2 19 1 potato 9.5
## 34 1 2 19 2 potato 11.3
## 35 1 2 31 1 potato 10.6
## 36 1 2 31 2 potato 11.4
## 37 1 2 51 1 potato 11.7
## 38 1 2 51 2 potato 8.5
## 39 1 2 52 1 potato 10.4
## 40 1 2 52 2 potato 7.1
## 41 1 2 63 1 potato 13.1
## 42 1 2 63 2 potato 7.7
## 43 1 2 78 1 potato 9.1
## 44 1 2 78 2 potato 4.3
## 45 1 2 79 1 potato 8.3
## 46 1 2 79 2 potato 5.1
## 47 1 2 86 1 potato 6.1
## 48 1 2 86 2 potato 3.2
## 49 1 3 3 1 potato 14.1
## 50 1 3 3 2 potato 9.5
## 51 1 3 10 1 potato 11.3
## 52 1 3 10 2 potato 10.1
## 53 1 3 15 1 potato 5.8
## 54 1 3 15 2 potato 8.0
## 55 1 3 16 1 potato 7.8
## 56 1 3 16 2 potato 5.2
## 57 1 3 19 1 potato 7.2
## 58 1 3 19 2 potato 11.1
## 59 1 3 31 1 potato 7.1
## 60 1 3 31 2 potato 12.5
## 61 1 3 51 1 potato 13.6
## 62 1 3 51 2 potato 8.5
## 63 1 3 52 1 potato 10.6
## 64 1 3 52 2 potato 10.3
## 65 1 3 63 1 potato 8.6
## 66 1 3 63 2 potato 10.3
## 67 1 3 78 1 potato 7.4
## 68 1 3 78 2 potato 5.0
## 69 1 3 79 1 potato 8.8
## 70 1 3 79 2 potato 8.3
## 71 1 3 86 1 potato 6.7
## 72 1 3 86 2 potato 7.9
## 73 2 1 3 1 potato 9.0
## 74 2 1 3 2 potato 5.5
## 75 2 1 10 1 potato 8.0
## 76 2 1 10 2 potato 10.2
## 77 2 1 15 1 potato 5.3
## 78 2 1 15 2 potato 7.3
## 79 2 1 16 1 potato 4.1
## 80 2 1 16 2 potato 11.0
## 81 2 1 19 1 potato 8.7
## 82 2 1 19 2 potato 11.0
## 83 2 1 31 1 potato 9.7
## 84 2 1 31 2 potato 4.1
## 85 2 1 51 1 potato 9.4
## 86 2 1 51 2 potato 14.3
## 87 2 1 52 1 potato 6.5
## 88 2 1 52 2 potato 8.2
## 89 2 1 63 1 potato 8.9
## 90 2 1 63 2 potato 8.7
## 91 2 1 78 1 potato 3.3
## 92 2 1 78 2 potato 4.5
## 93 2 1 79 1 potato 5.9
## 94 2 1 79 2 potato 6.0
## 95 2 1 86 1 potato 6.7
## 96 2 1 86 2 potato 5.9
## 97 2 2 3 1 potato 14.1
## 98 2 2 3 2 potato 3.3
## 99 2 2 10 1 potato 11.2
## 100 2 2 10 2 potato 8.2
## 101 2 2 15 1 potato 12.7
## 102 2 2 15 2 potato 3.9
## 103 2 2 16 1 potato 5.4
## 104 2 2 16 2 potato 2.6
## 105 2 2 19 1 potato 11.2
## 106 2 2 19 2 potato 4.3
## 107 2 2 31 1 potato 10.6
## 108 2 2 31 2 potato 6.6
## 109 2 2 51 1 potato 9.2
## 110 2 2 51 2 potato 11.6
## 111 2 2 52 1 potato 9.7
## 112 2 2 52 2 potato 10.2
## 113 2 2 63 1 potato 12.2
## 114 2 2 63 2 potato 10.7
## 115 2 2 78 1 potato 6.7
## 116 2 2 78 2 potato 7.0
## 117 2 2 79 1 potato 10.9
## 118 2 2 79 2 potato 10.4
## 119 2 2 86 1 potato 3.9
## 120 2 2 86 2 potato 8.3
## 121 2 3 3 1 potato 6.5
## 122 2 3 3 2 potato 13.8
## 123 2 3 10 1 potato 10.3
## 124 2 3 10 2 potato 10.2
## 125 2 3 15 1 potato 6.3
## 126 2 3 15 2 potato 10.4
## 127 2 3 16 1 potato 8.2
## 128 2 3 16 2 potato 1.5
## 129 2 3 19 1 potato 11.4
## 130 2 3 19 2 potato 6.0
## 131 2 3 31 1 potato 10.5
## 132 2 3 31 2 potato 8.1
## 133 2 3 51 1 potato 12.2
## 134 2 3 51 2 potato 12.1
## 135 2 3 52 1 potato 10.7
## 136 2 3 52 2 potato 9.1
## 137 2 3 63 1 potato 6.2
## 138 2 3 63 2 potato 6.5
## 139 2 3 78 1 potato 2.7
## 140 2 3 78 2 potato 4.4
## 141 2 3 79 1 potato 7.7
## 142 2 3 79 2 potato 6.7
## 143 2 3 86 1 potato 7.7
## 144 2 3 86 2 potato 4.0
## 145 3 1 3 1 potato 11.8
## 146 3 1 3 2 potato 7.8
## 147 3 1 10 1 potato 9.3
## 148 3 1 10 2 potato 9.1
## 149 3 1 15 1 potato 3.4
## 150 3 1 15 2 potato 5.7
## 151 3 1 16 1 potato 1.5
## 152 3 1 16 2 potato 4.2
## 153 3 1 19 1 potato 11.2
## 154 3 1 19 2 potato 11.8
## 155 3 1 31 1 potato 8.2
## 156 3 1 31 2 potato 8.8
## 157 3 1 51 1 potato 6.1
## 158 3 1 51 2 potato 10.1
## 159 3 1 52 1 potato 6.6
## 160 3 1 52 2 potato 10.4
## 161 3 1 63 1 potato 8.9
## 162 3 1 63 2 potato 10.8
## 163 3 1 78 1 potato 2.5
## 164 3 1 78 2 potato 6.3
## 165 3 1 79 1 potato 8.6
## 166 3 1 79 2 potato 3.8
## 167 3 1 86 1 potato 9.0
## 168 3 1 86 2 potato 10.6
## 169 3 2 3 1 potato 4.0
## 170 3 2 3 2 potato 9.9
## 171 3 2 10 1 potato 10.1
## 172 3 2 10 2 potato 9.0
## 173 3 2 15 1 potato 8.3
## 174 3 2 15 2 potato 6.1
## 175 3 2 16 1 potato 2.6
## 176 3 2 16 2 potato 9.6
## 177 3 2 19 1 potato 4.8
## 178 3 2 19 2 potato 5.6
## 179 3 2 31 1 potato 10.2
## 180 3 2 31 2 potato 10.0
## 181 3 2 51 1 potato 6.6
## 182 3 2 51 2 potato 9.3
## 183 3 2 52 1 potato 7.2
## 184 3 2 52 2 potato 8.2
## 185 3 2 63 1 potato 12.8
## 186 3 2 63 2 potato 10.7
## 187 3 2 78 1 potato 7.5
## 188 3 2 78 2 potato 2.4
## 189 3 2 79 1 potato 8.4
## 190 3 2 79 2 potato 5.4
## 191 3 2 86 1 potato 9.4
## 192 3 2 86 2 potato 5.2
## 193 3 3 3 1 potato 7.3
## 194 3 3 3 2 potato 7.3
## 195 3 3 10 1 potato 9.6
## 196 3 3 10 2 potato 11.0
## 197 3 3 15 1 potato 5.2
## 198 3 3 15 2 potato 6.5
## 199 3 3 16 1 potato 10.6
## 200 3 3 16 2 potato 8.6
## 201 3 3 19 1 potato 6.4
## 202 3 3 19 2 potato 9.7
## 203 3 3 31 1 potato 7.6
## 204 3 3 31 2 potato 8.7
## 205 3 3 51 1 potato 9.1
## 206 3 3 51 2 potato 9.3
## 207 3 3 52 1 potato 8.1
## 208 3 3 52 2 potato 10.2
## 209 3 3 63 1 potato 9.0
## 210 3 3 63 2 potato 9.1
## 211 3 3 78 1 potato 9.2
## 212 3 3 78 2 potato 2.0
## 213 3 3 79 1 potato 7.5
## 214 3 3 79 2 potato 8.7
## 215 3 3 86 1 potato 5.2
## 216 3 3 86 2 potato 5.7
## 217 4 1 3 1 potato 13.6
## 218 4 1 3 2 potato 5.3
## 219 4 1 10 1 potato 8.1
## 220 4 1 10 2 potato 9.1
## 221 4 1 15 1 potato 8.1
## 222 4 1 15 2 potato 7.2
## 223 4 1 16 1 potato 6.8
## 224 4 1 16 2 potato 10.5
## 225 4 1 19 1 potato 8.9
## 226 4 1 19 2 potato 6.9
## 227 4 1 31 1 potato 11.7
## 228 4 1 31 2 potato 3.4
## 229 4 1 51 1 potato 9.3
## 230 4 1 51 2 potato 13.2
## 231 4 1 52 1 potato 8.5
## 232 4 1 52 2 potato 8.9
## 233 4 1 63 1 potato 10.4
## 234 4 1 63 2 potato 11.4
## 235 4 1 78 1 potato 9.4
## 236 4 1 78 2 potato 3.2
## 237 4 1 79 1 potato 8.4
## 238 4 1 79 2 potato 7.0
## 239 4 1 86 1 potato 8.3
## 240 4 1 86 2 potato 4.1
## 241 4 2 3 1 potato 12.9
## 242 4 2 3 2 potato 12.7
## 243 4 2 10 1 potato 8.4
## 244 4 2 10 2 potato 9.0
## 245 4 2 15 1 potato 5.1
## 246 4 2 15 2 potato 8.7
## 247 4 2 16 1 potato 3.8
## 248 4 2 16 2 potato 7.4
## 249 4 2 19 1 potato 10.5
## 250 4 2 19 2 potato 12.9
## 251 4 2 31 1 potato 6.8
## 252 4 2 31 2 potato 10.5
## 253 4 2 51 1 potato 10.9
## 254 4 2 51 2 potato 13.2
## 255 4 2 52 1 potato 6.8
## 256 4 2 52 2 potato 8.3
## 257 4 2 63 1 potato 8.0
## 258 4 2 63 2 potato 11.6
## 259 4 2 78 1 potato 3.9
## 260 4 2 78 2 potato 1.1
## 261 4 2 79 1 potato 8.5
## 262 4 2 79 2 potato 10.7
## 263 4 2 86 1 potato 2.7
## 264 4 2 86 2 potato 2.5
## 265 4 3 3 1 potato 1.5
## 266 4 3 3 2 potato 5.9
## 267 4 3 10 1 potato 10.3
## 268 4 3 10 2 potato 9.5
## 269 4 3 15 1 potato 5.2
## 270 4 3 15 2 potato 7.0
## 271 4 3 16 1 potato 12.7
## 272 4 3 16 2 potato 6.8
## 273 4 3 19 1 potato 5.8
## 274 4 3 19 2 potato 4.7
## 275 4 3 31 1 potato 11.0
## 276 4 3 31 2 potato 9.2
## 277 4 3 51 1 potato 7.0
## 278 4 3 51 2 potato 10.3
## 279 4 3 52 1 potato 3.3
## 280 4 3 52 2 potato 5.2
## 281 4 3 63 1 potato 5.6
## 282 4 3 63 2 potato 5.8
## 283 4 3 78 1 potato 7.7
## 284 4 3 78 2 potato 3.5
## 285 4 3 79 1 potato 5.7
## 286 4 3 79 2 potato 9.2
## 287 4 3 86 1 potato 1.4
## 288 4 3 86 2 potato 2.5
## 289 5 1 3 1 potato 14.0
## 290 5 1 3 2 potato 12.9
## 291 5 1 10 1 potato 9.6
## 292 5 1 10 2 potato 8.7
## 293 5 1 15 1 potato 4.1
## 294 5 1 15 2 potato 3.2
## 295 5 1 16 1 potato 10.1
## 296 5 1 16 2 potato 10.5
## 297 5 1 19 1 potato 5.3
## 298 5 1 19 2 potato 9.9
## 299 5 1 31 1 potato 11.2
## 300 5 1 31 2 potato 9.9
## 301 5 1 51 1 potato 9.2
## 302 5 1 51 2 potato 12.5
## 303 5 1 52 1 potato 4.1
## 304 5 1 52 2 potato 6.4
## 305 5 1 63 1 potato 9.8
## 306 5 1 63 2 potato 4.2
## 307 5 1 78 1 potato 3.3
## 308 5 1 78 2 potato 5.0
## 309 5 1 79 1 potato 8.0
## 310 5 1 79 2 potato 8.1
## 311 5 1 86 1 potato 2.2
## 312 5 1 86 2 potato 3.6
## 313 5 2 3 1 potato 3.4
## 314 5 2 3 2 potato 13.7
## 315 5 2 10 1 potato 9.2
## 316 5 2 10 2 potato 8.5
## 317 5 2 15 1 potato 4.1
## 318 5 2 15 2 potato 1.5
## 319 5 2 16 1 potato 13.4
## 320 5 2 16 2 potato 11.0
## 321 5 2 19 1 potato 7.6
## 322 5 2 19 2 potato 5.5
## 323 5 2 31 1 potato 5.4
## 324 5 2 31 2 potato 6.5
## 325 5 2 51 1 potato 8.0
## 326 5 2 51 2 potato 12.7
## 327 5 2 52 1 potato 4.9
## 328 5 2 52 2 potato 5.9
## 329 5 2 63 1 potato 4.4
## 330 5 2 63 2 potato 7.5
## 331 5 2 78 1 potato 5.6
## 332 5 2 78 2 potato 2.4
## 333 5 2 79 1 potato 10.1
## 334 5 2 79 2 potato 6.1
## 335 5 2 86 1 potato 5.8
## 336 5 2 86 2 potato 3.2
## 337 5 3 3 1 potato 4.0
## 338 5 3 3 2 potato 10.1
## 339 5 3 10 1 potato 10.7
## 340 5 3 10 2 potato 9.0
## 341 5 3 15 1 potato NA
## 342 5 3 15 2 potato 3.6
## 343 5 3 16 1 potato 5.2
## 344 5 3 16 2 potato 6.7
## 345 5 3 19 1 potato 10.2
## 346 5 3 19 2 potato 7.1
## 347 5 3 31 1 potato 12.3
## 348 5 3 31 2 potato 11.5
## 349 5 3 51 1 potato 10.6
## 350 5 3 51 2 potato 7.5
## 351 5 3 52 1 potato 6.3
## 352 5 3 52 2 potato 4.0
## 353 5 3 63 1 potato 9.1
## 354 5 3 63 2 potato 9.0
## 355 5 3 78 1 potato 3.4
## 356 5 3 78 2 potato 1.5
## 357 5 3 79 1 potato 7.9
## 358 5 3 79 2 potato 9.4
## 359 5 3 86 1 potato 5.4
## 360 5 3 86 2 potato 3.6
## 361 6 1 3 1 potato 0.4
## 362 6 1 3 2 potato 3.3
## 363 6 1 10 1 potato 13.2
## 364 6 1 10 2 potato 10.0
## 365 6 1 15 1 potato 0.0
## 366 6 1 15 2 potato 2.6
## 367 6 1 16 1 potato 4.9
## 368 6 1 16 2 potato 8.9
## 369 6 1 19 1 potato 12.2
## 370 6 1 19 2 potato 11.1
## 371 6 1 31 1 potato 12.0
## 372 6 1 31 2 potato 8.2
## 373 6 1 51 1 potato 10.2
## 374 6 1 51 2 potato 8.5
## 375 6 1 52 1 potato 3.8
## 376 6 1 52 2 potato 3.7
## 377 6 1 63 1 potato 3.1
## 378 6 1 63 2 potato 4.2
## 379 6 1 78 1 potato 1.5
## 380 6 1 78 2 potato 1.1
## 381 6 1 79 1 potato 11.4
## 382 6 1 79 2 potato 7.9
## 383 6 1 86 1 potato 1.0
## 384 6 1 86 2 potato 2.7
## 385 6 2 3 1 potato 7.3
## 386 6 2 3 2 potato 1.8
## 387 6 2 10 1 potato 10.7
## 388 6 2 10 2 potato 11.4
## 389 6 2 15 1 potato 3.4
## 390 6 2 15 2 potato 4.6
## 391 6 2 16 1 potato 11.8
## 392 6 2 16 2 potato 10.8
## 393 6 2 19 1 potato 7.6
## 394 6 2 19 2 potato 11.1
## 395 6 2 31 1 potato 11.4
## 396 6 2 31 2 potato 6.9
## 397 6 2 51 1 potato 10.6
## 398 6 2 51 2 potato 6.3
## 399 6 2 52 1 potato 6.3
## 400 6 2 52 2 potato 5.6
## 401 6 2 63 1 potato 5.2
## 402 6 2 63 2 potato 6.1
## 403 6 2 78 1 potato 3.4
## 404 6 2 78 2 potato 1.3
## 405 6 2 79 1 potato 5.7
## 406 6 2 79 2 potato 9.4
## 407 6 2 86 1 potato 6.4
## 408 6 2 86 2 potato 3.3
## 409 6 3 3 1 potato 2.2
## 410 6 3 3 2 potato 5.3
## 411 6 3 10 1 potato 10.8
## 412 6 3 10 2 potato 11.5
## 413 6 3 15 1 potato 1.0
## 414 6 3 15 2 potato 4.3
## 415 6 3 16 1 potato 5.3
## 416 6 3 16 2 potato 12.1
## 417 6 3 19 1 potato 10.6
## 418 6 3 19 2 potato 6.1
## 419 6 3 31 1 potato 8.3
## 420 6 3 31 2 potato 8.1
## 421 6 3 51 1 potato 12.4
## 422 6 3 51 2 potato 8.5
## 423 6 3 52 1 potato 4.4
## 424 6 3 52 2 potato 3.9
## 425 6 3 63 1 potato 6.5
## 426 6 3 63 2 potato 11.4
## 427 6 3 78 1 potato 5.5
## 428 6 3 78 2 potato 4.3
## 429 6 3 79 1 potato 8.4
## 430 6 3 79 2 potato 8.8
## 431 6 3 86 1 potato 2.2
## 432 6 3 86 2 potato 4.1
## 433 7 1 3 1 potato 2.9
## 434 7 1 3 2 potato 0.8
## 435 7 1 10 1 potato 11.0
## 436 7 1 10 2 potato 8.7
## 437 7 1 15 1 potato 1.2
## 438 7 1 15 2 potato 2.9
## 439 7 1 16 1 potato 9.0
## 440 7 1 16 2 potato 7.2
## 441 7 1 19 1 potato 7.0
## 442 7 1 19 2 potato 5.5
## 443 7 1 31 1 potato 12.2
## 444 7 1 31 2 potato 9.4
## 445 7 1 51 1 potato 8.6
## 446 7 1 51 2 potato 14.1
## 447 7 1 52 1 potato 5.8
## 448 7 1 52 2 potato 3.2
## 449 7 1 63 1 potato 8.3
## 450 7 1 63 2 potato 6.2
## 451 7 1 78 1 potato 4.9
## 452 7 1 78 2 potato 0.5
## 453 7 1 79 1 potato 5.1
## 454 7 1 79 2 potato 9.9
## 455 7 1 86 1 potato 5.2
## 456 7 1 86 2 potato 1.2
## 457 7 2 3 1 potato 1.5
## 458 7 2 3 2 potato 3.5
## 459 7 2 10 1 potato 10.5
## 460 7 2 10 2 potato 9.6
## 461 7 2 15 1 potato 0.4
## 462 7 2 15 2 potato 1.7
## 463 7 2 16 1 potato 3.7
## 464 7 2 16 2 potato 8.1
## 465 7 2 19 1 potato 6.5
## 466 7 2 19 2 potato 9.3
## 467 7 2 31 1 potato 6.1
## 468 7 2 31 2 potato 7.0
## 469 7 2 51 1 potato 7.3
## 470 7 2 51 2 potato 12.4
## 471 7 2 52 1 potato 4.1
## 472 7 2 52 2 potato 4.2
## 473 7 2 63 1 potato 10.4
## 474 7 2 63 2 potato 9.8
## 475 7 2 78 1 potato 1.3
## 476 7 2 78 2 potato 1.4
## 477 7 2 79 1 potato 7.3
## 478 7 2 79 2 potato 6.6
## 479 7 2 86 1 potato 1.2
## 480 7 2 86 2 potato 3.6
## 481 7 3 3 1 potato 0.9
## 482 7 3 3 2 potato 2.6
## 483 7 3 10 1 potato 7.1
## 484 7 3 10 2 potato 9.5
## 485 7 3 15 1 potato 1.2
## 486 7 3 15 2 potato 1.0
## 487 7 3 16 1 potato 12.7
## 488 7 3 16 2 potato 10.8
## 489 7 3 19 1 potato 8.6
## 490 7 3 19 2 potato 9.0
## 491 7 3 31 1 potato 6.4
## 492 7 3 31 2 potato 12.0
## 493 7 3 51 1 potato 9.4
## 494 7 3 51 2 potato 8.7
## 495 7 3 52 1 potato 6.4
## 496 7 3 52 2 potato 5.7
## 497 7 3 63 1 potato 9.5
## 498 7 3 63 2 potato 10.7
## 499 7 3 78 1 potato 4.3
## 500 7 3 78 2 potato 0.2
## 501 7 3 79 1 potato 7.2
## 502 7 3 79 2 potato 6.5
## 503 7 3 86 1 potato 3.0
## 504 7 3 86 2 potato 2.4
## 505 8 1 3 1 potato 3.5
## 506 8 1 3 2 potato 0.6
## 507 8 1 10 1 potato 10.2
## 508 8 1 10 2 potato 8.6
## 509 8 1 15 1 potato 1.9
## 510 8 1 15 2 potato 0.6
## 511 8 1 16 1 potato 2.4
## 512 8 1 16 2 potato 0.9
## 513 8 1 19 1 potato 5.4
## 514 8 1 19 2 potato 11.0
## 515 8 1 31 1 potato 4.0
## 516 8 1 31 2 potato 6.6
## 517 8 1 51 1 potato 14.9
## 518 8 1 51 2 potato 11.5
## 519 8 1 52 1 potato 2.1
## 520 8 1 52 2 potato 1.7
## 521 8 1 63 1 potato 5.9
## 522 8 1 63 2 potato 3.8
## 523 8 1 78 1 potato 1.5
## 524 8 1 78 2 potato 1.6
## 525 8 1 79 1 potato 10.5
## 526 8 1 79 2 potato 9.8
## 527 8 1 86 1 potato 3.8
## 528 8 1 86 2 potato 1.4
## 529 8 2 3 1 potato 0.9
## 530 8 2 3 2 potato 0.5
## 531 8 2 10 1 potato 10.9
## 532 8 2 10 2 potato 10.3
## 533 8 2 15 1 potato 3.2
## 534 8 2 15 2 potato 2.3
## 535 8 2 16 1 potato 4.5
## 536 8 2 16 2 potato 5.3
## 537 8 2 19 1 potato 11.2
## 538 8 2 19 2 potato 8.7
## 539 8 2 31 1 potato 7.8
## 540 8 2 31 2 potato 9.9
## 541 8 2 51 1 potato 13.8
## 542 8 2 51 2 potato 8.5
## 543 8 2 52 1 potato 3.9
## 544 8 2 52 2 potato 0.0
## 545 8 2 63 1 potato 3.9
## 546 8 2 63 2 potato 5.7
## 547 8 2 78 1 potato 4.0
## 548 8 2 78 2 potato 2.1
## 549 8 2 79 1 potato 8.2
## 550 8 2 79 2 potato 5.7
## 551 8 2 86 1 potato 1.3
## 552 8 2 86 2 potato 2.8
## 553 8 3 3 1 potato 1.5
## 554 8 3 3 2 potato 0.5
## 555 8 3 10 1 potato 10.0
## 556 8 3 10 2 potato 10.9
## 557 8 3 15 1 potato 0.6
## 558 8 3 15 2 potato 0.2
## 559 8 3 16 1 potato 2.2
## 560 8 3 16 2 potato 3.3
## 561 8 3 19 1 potato 11.1
## 562 8 3 19 2 potato 11.5
## 563 8 3 31 1 potato 4.2
## 564 8 3 31 2 potato 8.1
## 565 8 3 51 1 potato 14.5
## 566 8 3 51 2 potato 14.0
## 567 8 3 52 1 potato 0.0
## 568 8 3 52 2 potato 3.2
## 569 8 3 63 1 potato 7.8
## 570 8 3 63 2 potato 8.6
## 571 8 3 78 1 potato 0.8
## 572 8 3 78 2 potato 3.2
## 573 8 3 79 1 potato 6.8
## 574 8 3 79 2 potato 5.7
## 575 8 3 86 1 potato 1.4
## 576 8 3 86 2 potato 1.4
## 577 9 1 3 1 potato 1.1
## 578 9 1 3 2 potato 2.5
## 579 9 1 10 1 potato 10.5
## 580 9 1 10 2 potato 11.2
## 581 9 1 15 1 potato 0.2
## 582 9 1 15 2 potato 1.7
## 583 9 1 16 1 potato 5.4
## 584 9 1 16 2 potato 8.5
## 585 9 1 19 1 potato 9.6
## 586 9 1 19 2 potato 9.0
## 587 9 1 51 1 potato 10.2
## 588 9 1 51 2 potato 12.7
## 589 9 1 52 1 potato 5.1
## 590 9 1 52 2 potato 3.0
## 591 9 1 63 1 potato 1.7
## 592 9 1 63 2 potato 5.3
## 593 9 1 78 1 potato 3.5
## 594 9 1 78 2 potato 1.0
## 595 9 1 79 1 potato 10.1
## 596 9 1 79 2 potato 9.1
## 597 9 2 3 1 potato 2.7
## 598 9 2 3 2 potato 1.8
## 599 9 2 10 1 potato 10.4
## 600 9 2 10 2 potato 11.0
## 601 9 2 15 1 potato 0.2
## 602 9 2 15 2 potato 0.0
## 603 9 2 16 1 potato 8.5
## 604 9 2 16 2 potato 3.8
## 605 9 2 19 1 potato 11.1
## 606 9 2 19 2 potato 6.2
## 607 9 2 51 1 potato 7.3
## 608 9 2 51 2 potato 10.6
## 609 9 2 52 1 potato 1.8
## 610 9 2 52 2 potato 1.6
## 611 9 2 63 1 potato 1.8
## 612 9 2 63 2 potato 9.1
## 613 9 2 78 1 potato 2.0
## 614 9 2 78 2 potato 3.4
## 615 9 2 79 1 potato 6.7
## 616 9 2 79 2 potato 9.4
## 617 9 3 3 1 potato 1.6
## 618 9 3 3 2 potato 0.7
## 619 9 3 10 1 potato 10.2
## 620 9 3 10 2 potato 7.3
## 621 9 3 15 1 potato 0.7
## 622 9 3 15 2 potato 1.5
## 623 9 3 16 1 potato 7.5
## 624 9 3 16 2 potato 3.6
## 625 9 3 19 1 potato 7.9
## 626 9 3 19 2 potato 9.1
## 627 9 3 51 1 potato 8.2
## 628 9 3 51 2 potato 8.9
## 629 9 3 52 1 potato 1.1
## 630 9 3 52 2 potato 4.0
## 631 9 3 63 1 potato 7.5
## 632 9 3 63 2 potato 6.0
## 633 9 3 78 1 potato 7.9
## 634 9 3 78 2 potato 0.0
## 635 9 3 79 1 potato 7.8
## 636 9 3 79 2 potato 8.1
## 637 10 1 10 1 potato 10.6
## 638 10 1 10 2 potato 12.1
## 639 10 1 15 1 potato 0.1
## 640 10 1 15 2 potato 1.7
## 641 10 1 16 1 potato 3.0
## 642 10 1 16 2 potato 3.8
## 643 10 1 19 1 potato 11.4
## 644 10 1 19 2 potato 11.8
## 645 10 1 31 1 potato 9.5
## 646 10 1 31 2 potato 5.3
## 647 10 1 51 1 potato 7.6
## 648 10 1 51 2 potato 12.3
## 649 10 1 52 1 potato 0.4
## 650 10 1 52 2 potato 0.0
## 651 10 1 63 1 potato 6.5
## 652 10 1 63 2 potato 6.2
## 653 10 1 78 1 potato 1.2
## 654 10 1 78 2 potato 4.4
## 655 10 1 86 1 potato 0.7
## 656 10 1 86 2 potato 0.7
## 657 10 2 10 1 potato 10.7
## 658 10 2 10 2 potato 10.5
## 659 10 2 15 1 potato 4.8
## 660 10 2 15 2 potato 1.1
## 661 10 2 16 1 potato 4.2
## 662 10 2 16 2 potato 2.9
## 663 10 2 19 1 potato 7.0
## 664 10 2 19 2 potato 10.9
## 665 10 2 31 1 potato 4.6
## 666 10 2 31 2 potato 2.3
## 667 10 2 51 1 potato 11.3
## 668 10 2 51 2 potato 9.9
## 669 10 2 52 1 potato 0.3
## 670 10 2 52 2 potato 3.8
## 671 10 2 63 1 potato 8.6
## 672 10 2 63 2 potato 9.0
## 673 10 2 78 1 potato 5.2
## 674 10 2 78 2 potato 1.5
## 675 10 2 86 1 potato 2.0
## 676 10 2 86 2 potato 1.0
## 677 10 3 10 1 potato 11.3
## 678 10 3 10 2 potato 10.0
## 679 10 3 15 1 potato 2.5
## 680 10 3 15 2 potato 4.3
## 681 10 3 16 1 potato 3.9
## 682 10 3 16 2 potato 2.5
## 683 10 3 19 1 potato 9.1
## 684 10 3 19 2 potato 12.2
## 685 10 3 31 1 potato 6.5
## 686 10 3 31 2 potato 10.4
## 687 10 3 51 1 potato 8.0
## 688 10 3 51 2 potato 11.6
## 689 10 3 52 1 potato 1.2
## 690 10 3 52 2 potato 1.8
## 691 10 3 63 1 potato 7.0
## 692 10 3 63 2 potato 7.0
## 693 10 3 78 1 potato 3.7
## 694 10 3 78 2 potato 3.3
## 695 10 3 86 1 potato 2.5
## 696 10 3 86 2 potato 2.5
## 697 1 1 3 1 buttery 0.0
## 698 1 1 3 2 buttery 0.0
## 699 1 1 10 1 buttery 6.4
## 700 1 1 10 2 buttery 5.9
## 701 1 1 15 1 buttery 0.1
## 702 1 1 15 2 buttery 3.0
## 703 1 1 16 1 buttery 2.6
## 704 1 1 16 2 buttery 4.4
## 705 1 1 19 1 buttery 3.2
## 706 1 1 19 2 buttery 0.0
## 707 1 1 31 1 buttery 0.0
## 708 1 1 31 2 buttery 2.5
## 709 1 1 51 1 buttery 1.7
## 710 1 1 51 2 buttery 4.2
## 711 1 1 52 1 buttery 0.0
## 712 1 1 52 2 buttery 3.1
## 713 1 1 63 1 buttery 0.0
## 714 1 1 63 2 buttery 0.0
## 715 1 1 78 1 buttery 1.2
## 716 1 1 78 2 buttery 0.6
## 717 1 1 79 1 buttery 0.0
## 718 1 1 79 2 buttery 0.4
## 719 1 1 86 1 buttery 1.2
## 720 1 1 86 2 buttery 2.6
## 721 1 2 3 1 buttery 0.0
## 722 1 2 3 2 buttery 0.1
## 723 1 2 10 1 buttery 5.2
## 724 1 2 10 2 buttery 10.1
## 725 1 2 15 1 buttery 3.6
## 726 1 2 15 2 buttery 2.7
## 727 1 2 16 1 buttery 3.5
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## 729 1 2 19 1 buttery 2.5
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## 733 1 2 51 1 buttery 4.1
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## 735 1 2 52 1 buttery 4.4
## 736 1 2 52 2 buttery 1.1
## 737 1 2 63 1 buttery 0.3
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## 740 1 2 78 2 buttery 0.1
## 741 1 2 79 1 buttery 0.8
## 742 1 2 79 2 buttery 4.5
## 743 1 2 86 1 buttery 4.4
## 744 1 2 86 2 buttery 4.3
## 745 1 3 3 1 buttery 0.0
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## 751 1 3 16 1 buttery 0.2
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## 753 1 3 19 1 buttery 3.4
## 754 1 3 19 2 buttery 2.8
## 755 1 3 31 1 buttery 0.7
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## 757 1 3 51 1 buttery 1.4
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## 759 1 3 52 1 buttery 2.0
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## 773 2 1 15 1 buttery 1.4
## 774 2 1 15 2 buttery 2.3
## 775 2 1 16 1 buttery 5.1
## 776 2 1 16 2 buttery 3.8
## 777 2 1 19 1 buttery 0.0
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## 779 2 1 31 1 buttery 3.3
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## 783 2 1 52 1 buttery 1.6
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## 788 2 1 78 2 buttery 1.7
## 789 2 1 79 1 buttery 0.0
## 790 2 1 79 2 buttery 0.0
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## 807 2 2 52 1 buttery 3.6
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## 812 2 2 78 2 buttery 0.0
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## 814 2 2 79 2 buttery 1.1
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## 817 2 3 3 1 buttery 0.6
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## 821 2 3 15 1 buttery 3.6
## 822 2 3 15 2 buttery 3.2
## 823 2 3 16 1 buttery 7.3
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## 825 2 3 19 1 buttery 3.6
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## 831 2 3 52 1 buttery 2.8
## 832 2 3 52 2 buttery 1.9
## 833 2 3 63 1 buttery 0.0
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## 836 2 3 78 2 buttery 0.0
## 837 2 3 79 1 buttery 1.1
## 838 2 3 79 2 buttery 0.0
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## 840 2 3 86 2 buttery 5.7
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## 845 3 1 15 1 buttery 0.2
## 846 3 1 15 2 buttery 2.7
## 847 3 1 16 1 buttery 0.2
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## 849 3 1 19 1 buttery 9.6
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## 852 3 1 31 2 buttery 0.0
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## 856 3 1 52 2 buttery 2.9
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## 860 3 1 78 2 buttery 1.0
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## 885 3 2 79 1 buttery 0.0
## 886 3 2 79 2 buttery 0.0
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## 946 4 2 19 2 buttery 1.4
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## 956 4 2 78 2 buttery 0.0
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## 966 4 3 15 2 buttery 0.3
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## 978 4 3 63 2 buttery 0.0
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## 982 4 3 79 2 buttery 0.0
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## 984 4 3 86 2 buttery 0.0
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## 988 5 1 10 2 buttery 5.4
## 989 5 1 15 1 buttery 0.3
## 990 5 1 15 2 buttery 0.2
## 991 5 1 16 1 buttery 0.4
## 992 5 1 16 2 buttery 3.9
## 993 5 1 19 1 buttery 0.9
## 994 5 1 19 2 buttery 1.7
## 995 5 1 31 1 buttery 0.0
## 996 5 1 31 2 buttery 0.3
## 997 5 1 51 1 buttery 2.4
## 998 5 1 51 2 buttery 4.7
## 999 5 1 52 1 buttery 0.0
## 1000 5 1 52 2 buttery 0.0
## 1001 5 1 63 1 buttery 0.0
## 1002 5 1 63 2 buttery 0.0
## 1003 5 1 78 1 buttery 1.1
## 1004 5 1 78 2 buttery 1.2
## 1005 5 1 79 1 buttery 0.8
## 1006 5 1 79 2 buttery 0.0
## 1007 5 1 86 1 buttery 0.0
## 1008 5 1 86 2 buttery 0.0
## 1009 5 2 3 1 buttery 0.0
## 1010 5 2 3 2 buttery 1.7
## 1011 5 2 10 1 buttery 7.5
## 1012 5 2 10 2 buttery 2.8
## 1013 5 2 15 1 buttery 0.2
## 1014 5 2 15 2 buttery 0.3
## 1015 5 2 16 1 buttery 5.0
## 1016 5 2 16 2 buttery 6.8
## 1017 5 2 19 1 buttery 5.0
## 1018 5 2 19 2 buttery 4.7
## 1019 5 2 31 1 buttery 0.0
## 1020 5 2 31 2 buttery 0.0
## 1021 5 2 51 1 buttery 2.9
## 1022 5 2 51 2 buttery 4.7
## 1023 5 2 52 1 buttery 0.0
## 1024 5 2 52 2 buttery 0.0
## 1025 5 2 63 1 buttery 0.0
## 1026 5 2 63 2 buttery 0.0
## 1027 5 2 78 1 buttery 0.0
## 1028 5 2 78 2 buttery 0.0
## 1029 5 2 79 1 buttery 0.0
## 1030 5 2 79 2 buttery 0.5
## 1031 5 2 86 1 buttery 1.5
## 1032 5 2 86 2 buttery 1.0
## 1033 5 3 3 1 buttery 0.0
## 1034 5 3 3 2 buttery 4.3
## 1035 5 3 10 1 buttery 8.8
## 1036 5 3 10 2 buttery 4.3
## 1037 5 3 15 1 buttery NA
## 1038 5 3 15 2 buttery 0.2
## 1039 5 3 16 1 buttery 1.0
## 1040 5 3 16 2 buttery 2.0
## 1041 5 3 19 1 buttery 2.9
## 1042 5 3 19 2 buttery 0.0
## 1043 5 3 31 1 buttery 2.7
## 1044 5 3 31 2 buttery 0.0
## 1045 5 3 51 1 buttery 4.4
## 1046 5 3 51 2 buttery 2.3
## 1047 5 3 52 1 buttery 0.1
## 1048 5 3 52 2 buttery 0.0
## 1049 5 3 63 1 buttery 0.0
## 1050 5 3 63 2 buttery 0.0
## 1051 5 3 78 1 buttery 0.8
## 1052 5 3 78 2 buttery 0.8
## 1053 5 3 79 1 buttery 0.0
## 1054 5 3 79 2 buttery 0.5
## 1055 5 3 86 1 buttery 4.0
## 1056 5 3 86 2 buttery 0.1
## 1057 6 1 3 1 buttery 1.2
## 1058 6 1 3 2 buttery 1.1
## 1059 6 1 10 1 buttery 11.2
## 1060 6 1 10 2 buttery 7.6
## 1061 6 1 15 1 buttery 0.3
## 1062 6 1 15 2 buttery 0.2
## 1063 6 1 16 1 buttery 0.3
## 1064 6 1 16 2 buttery 4.2
## 1065 6 1 19 1 buttery 9.5
## 1066 6 1 19 2 buttery 0.0
## 1067 6 1 31 1 buttery 0.0
## 1068 6 1 31 2 buttery 0.0
## 1069 6 1 51 1 buttery 4.7
## 1070 6 1 51 2 buttery 2.5
## 1071 6 1 52 1 buttery 0.0
## 1072 6 1 52 2 buttery 0.0
## 1073 6 1 63 1 buttery 0.0
## 1074 6 1 63 2 buttery 0.0
## 1075 6 1 78 1 buttery 0.0
## 1076 6 1 78 2 buttery 0.6
## 1077 6 1 79 1 buttery 0.4
## 1078 6 1 79 2 buttery 0.0
## 1079 6 1 86 1 buttery 0.0
## 1080 6 1 86 2 buttery 0.0
## 1081 6 2 3 1 buttery 0.7
## 1082 6 2 3 2 buttery 1.9
## 1083 6 2 10 1 buttery 7.7
## 1084 6 2 10 2 buttery 6.8
## 1085 6 2 15 1 buttery 0.3
## 1086 6 2 15 2 buttery 1.5
## 1087 6 2 16 1 buttery 2.7
## 1088 6 2 16 2 buttery 4.3
## 1089 6 2 19 1 buttery 0.0
## 1090 6 2 19 2 buttery 1.5
## 1091 6 2 31 1 buttery 0.8
## 1092 6 2 31 2 buttery 1.6
## 1093 6 2 51 1 buttery 3.1
## 1094 6 2 51 2 buttery 8.2
## 1095 6 2 52 1 buttery 0.8
## 1096 6 2 52 2 buttery 0.7
## 1097 6 2 63 1 buttery 0.0
## 1098 6 2 63 2 buttery 0.0
## 1099 6 2 78 1 buttery 0.0
## 1100 6 2 78 2 buttery 0.0
## 1101 6 2 79 1 buttery 0.0
## 1102 6 2 79 2 buttery 0.8
## 1103 6 2 86 1 buttery 5.3
## 1104 6 2 86 2 buttery 0.6
## 1105 6 3 3 1 buttery 1.0
## 1106 6 3 3 2 buttery 2.2
## 1107 6 3 10 1 buttery 5.4
## 1108 6 3 10 2 buttery 5.5
## 1109 6 3 15 1 buttery 0.1
## 1110 6 3 15 2 buttery 0.2
## 1111 6 3 16 1 buttery 0.8
## 1112 6 3 16 2 buttery 5.1
## 1113 6 3 19 1 buttery 1.0
## 1114 6 3 19 2 buttery 3.6
## 1115 6 3 31 1 buttery 0.0
## 1116 6 3 31 2 buttery 0.0
## 1117 6 3 51 1 buttery 4.5
## 1118 6 3 51 2 buttery 2.5
## 1119 6 3 52 1 buttery 0.0
## 1120 6 3 52 2 buttery 0.0
## 1121 6 3 63 1 buttery 0.0
## 1122 6 3 63 2 buttery 0.0
## 1123 6 3 78 1 buttery 0.0
## 1124 6 3 78 2 buttery 0.0
## 1125 6 3 79 1 buttery 0.0
## 1126 6 3 79 2 buttery 0.0
## 1127 6 3 86 1 buttery 1.2
## 1128 6 3 86 2 buttery 0.0
## 1129 7 1 3 1 buttery 0.0
## 1130 7 1 3 2 buttery 0.0
## 1131 7 1 10 1 buttery 6.4
## 1132 7 1 10 2 buttery 3.7
## 1133 7 1 15 1 buttery 0.1
## 1134 7 1 15 2 buttery 0.0
## 1135 7 1 16 1 buttery 2.6
## 1136 7 1 16 2 buttery 5.7
## 1137 7 1 19 1 buttery 3.2
## 1138 7 1 19 2 buttery 1.5
## 1139 7 1 31 1 buttery 0.0
## 1140 7 1 31 2 buttery 0.0
## 1141 7 1 51 1 buttery 1.7
## 1142 7 1 51 2 buttery 1.4
## 1143 7 1 52 1 buttery 0.0
## 1144 7 1 52 2 buttery 0.1
## 1145 7 1 63 1 buttery 0.0
## 1146 7 1 63 2 buttery 0.0
## 1147 7 1 78 1 buttery 1.2
## 1148 7 1 78 2 buttery 1.0
## 1149 7 1 79 1 buttery 0.0
## 1150 7 1 79 2 buttery 0.0
## 1151 7 1 86 1 buttery 1.2
## 1152 7 1 86 2 buttery 0.0
## 1153 7 2 3 1 buttery 0.6
## 1154 7 2 3 2 buttery 1.2
## 1155 7 2 10 1 buttery 9.0
## 1156 7 2 10 2 buttery 4.4
## 1157 7 2 15 1 buttery 0.1
## 1158 7 2 15 2 buttery 0.0
## 1159 7 2 16 1 buttery 1.6
## 1160 7 2 16 2 buttery 5.6
## 1161 7 2 19 1 buttery 0.0
## 1162 7 2 19 2 buttery 0.0
## 1163 7 2 31 1 buttery 0.0
## 1164 7 2 31 2 buttery 0.0
## 1165 7 2 51 1 buttery 2.5
## 1166 7 2 51 2 buttery 4.4
## 1167 7 2 52 1 buttery 0.3
## 1168 7 2 52 2 buttery 0.0
## 1169 7 2 63 1 buttery 0.5
## 1170 7 2 63 2 buttery 0.0
## 1171 7 2 78 1 buttery 0.0
## 1172 7 2 78 2 buttery 1.0
## 1173 7 2 79 1 buttery NA
## 1174 7 2 79 2 buttery 0.0
## 1175 7 2 86 1 buttery 0.0
## 1176 7 2 86 2 buttery 0.0
## 1177 7 3 3 1 buttery 0.7
## 1178 7 3 3 2 buttery 0.9
## 1179 7 3 10 1 buttery 2.7
## 1180 7 3 10 2 buttery 8.1
## 1181 7 3 15 1 buttery 0.0
## 1182 7 3 15 2 buttery 0.1
## 1183 7 3 16 1 buttery 5.1
## 1184 7 3 16 2 buttery 7.5
## 1185 7 3 19 1 buttery 0.0
## 1186 7 3 19 2 buttery 0.0
## 1187 7 3 31 1 buttery 0.0
## 1188 7 3 31 2 buttery 0.4
## 1189 7 3 51 1 buttery 2.2
## 1190 7 3 51 2 buttery 4.6
## 1191 7 3 52 1 buttery 0.3
## 1192 7 3 52 2 buttery 0.8
## 1193 7 3 63 1 buttery 0.0
## 1194 7 3 63 2 buttery 0.5
## 1195 7 3 78 1 buttery 0.0
## 1196 7 3 78 2 buttery 0.0
## 1197 7 3 79 1 buttery 0.0
## 1198 7 3 79 2 buttery 0.0
## 1199 7 3 86 1 buttery 2.3
## 1200 7 3 86 2 buttery 0.0
## 1201 8 1 3 1 buttery 0.5
## 1202 8 1 3 2 buttery 0.3
## 1203 8 1 10 1 buttery 8.2
## 1204 8 1 10 2 buttery 4.0
## 1205 8 1 15 1 buttery 1.9
## 1206 8 1 15 2 buttery 0.0
## 1207 8 1 16 1 buttery 1.0
## 1208 8 1 16 2 buttery 0.3
## 1209 8 1 19 1 buttery 3.5
## 1210 8 1 19 2 buttery 0.0
## 1211 8 1 31 1 buttery 0.0
## 1212 8 1 31 2 buttery 0.0
## 1213 8 1 51 1 buttery 0.8
## 1214 8 1 51 2 buttery 1.3
## 1215 8 1 52 1 buttery 0.0
## 1216 8 1 52 2 buttery 0.0
## 1217 8 1 63 1 buttery 0.0
## 1218 8 1 63 2 buttery 0.0
## 1219 8 1 78 1 buttery 0.0
## 1220 8 1 78 2 buttery 0.9
## 1221 8 1 79 1 buttery NA
## 1222 8 1 79 2 buttery 0.0
## 1223 8 1 86 1 buttery 0.0
## 1224 8 1 86 2 buttery 0.0
## 1225 8 2 3 1 buttery 0.3
## 1226 8 2 3 2 buttery 0.3
## 1227 8 2 10 1 buttery 4.6
## 1228 8 2 10 2 buttery 9.3
## 1229 8 2 15 1 buttery 0.1
## 1230 8 2 15 2 buttery 0.0
## 1231 8 2 16 1 buttery NA
## 1232 8 2 16 2 buttery 3.5
## 1233 8 2 19 1 buttery 2.2
## 1234 8 2 19 2 buttery 5.2
## 1235 8 2 31 1 buttery 1.1
## 1236 8 2 31 2 buttery 1.0
## 1237 8 2 51 1 buttery 1.4
## 1238 8 2 51 2 buttery 6.2
## 1239 8 2 52 1 buttery 0.0
## 1240 8 2 52 2 buttery 0.0
## 1241 8 2 63 1 buttery 0.0
## 1242 8 2 63 2 buttery 0.0
## 1243 8 2 78 1 buttery 0.0
## 1244 8 2 78 2 buttery 1.2
## 1245 8 2 79 1 buttery 0.0
## 1246 8 2 79 2 buttery 0.0
## 1247 8 2 86 1 buttery 0.0
## 1248 8 2 86 2 buttery 0.0
## 1249 8 3 3 1 buttery 0.6
## 1250 8 3 3 2 buttery 0.5
## 1251 8 3 10 1 buttery 6.0
## 1252 8 3 10 2 buttery 4.5
## 1253 8 3 15 1 buttery 0.0
## 1254 8 3 15 2 buttery 0.1
## 1255 8 3 16 1 buttery 0.9
## 1256 8 3 16 2 buttery 1.2
## 1257 8 3 19 1 buttery 2.9
## 1258 8 3 19 2 buttery 0.0
## 1259 8 3 31 1 buttery 0.0
## 1260 8 3 31 2 buttery 0.0
## 1261 8 3 51 1 buttery 5.5
## 1262 8 3 51 2 buttery 1.1
## 1263 8 3 52 1 buttery 0.0
## 1264 8 3 52 2 buttery 0.0
## 1265 8 3 63 1 buttery 0.0
## 1266 8 3 63 2 buttery 0.4
## 1267 8 3 78 1 buttery 0.0
## 1268 8 3 78 2 buttery 0.0
## 1269 8 3 79 1 buttery 0.0
## 1270 8 3 79 2 buttery 0.0
## 1271 8 3 86 1 buttery 0.0
## 1272 8 3 86 2 buttery 0.0
## 1273 9 1 3 1 buttery 0.4
## 1274 9 1 3 2 buttery 0.5
## 1275 9 1 10 1 buttery 8.5
## 1276 9 1 10 2 buttery 8.4
## 1277 9 1 15 1 buttery 0.1
## 1278 9 1 15 2 buttery 0.4
## 1279 9 1 16 1 buttery 6.7
## 1280 9 1 16 2 buttery 4.9
## 1281 9 1 19 1 buttery 0.0
## 1282 9 1 19 2 buttery 3.0
## 1283 9 1 51 1 buttery 0.0
## 1284 9 1 51 2 buttery 3.5
## 1285 9 1 52 1 buttery 0.2
## 1286 9 1 52 2 buttery 0.0
## 1287 9 1 63 1 buttery 0.0
## 1288 9 1 63 2 buttery 0.0
## 1289 9 1 78 1 buttery 0.0
## 1290 9 1 78 2 buttery 0.0
## 1291 9 1 79 1 buttery 0.0
## 1292 9 1 79 2 buttery 0.0
## 1293 9 2 3 1 buttery 0.2
## 1294 9 2 3 2 buttery 1.4
## 1295 9 2 10 1 buttery 6.7
## 1296 9 2 10 2 buttery 8.8
## 1297 9 2 15 1 buttery 0.1
## 1298 9 2 15 2 buttery 1.3
## 1299 9 2 16 1 buttery 2.8
## 1300 9 2 16 2 buttery 2.2
## 1301 9 2 19 1 buttery 3.6
## 1302 9 2 19 2 buttery 0.0
## 1303 9 2 51 1 buttery 2.3
## 1304 9 2 51 2 buttery 3.9
## 1305 9 2 52 1 buttery 0.0
## 1306 9 2 52 2 buttery 0.0
## 1307 9 2 63 1 buttery 0.0
## 1308 9 2 63 2 buttery 0.0
## 1309 9 2 78 1 buttery 0.0
## 1310 9 2 78 2 buttery 0.0
## 1311 9 2 79 1 buttery 0.0
## 1312 9 2 79 2 buttery 0.0
## 1313 9 3 3 1 buttery 0.5
## 1314 9 3 3 2 buttery 0.3
## 1315 9 3 10 1 buttery 8.5
## 1316 9 3 10 2 buttery 3.4
## 1317 9 3 15 1 buttery 0.0
## 1318 9 3 15 2 buttery 0.0
## 1319 9 3 16 1 buttery 2.6
## 1320 9 3 16 2 buttery 2.4
## 1321 9 3 19 1 buttery 0.0
## 1322 9 3 19 2 buttery 0.0
## 1323 9 3 51 1 buttery 0.4
## 1324 9 3 51 2 buttery 4.7
## 1325 9 3 52 1 buttery 0.0
## 1326 9 3 52 2 buttery 0.0
## 1327 9 3 63 1 buttery 0.0
## 1328 9 3 63 2 buttery 0.0
## 1329 9 3 78 1 buttery 1.0
## 1330 9 3 78 2 buttery 0.0
## 1331 9 3 79 1 buttery 0.0
## 1332 9 3 79 2 buttery 1.5
## 1333 10 1 10 1 buttery 7.1
## 1334 10 1 10 2 buttery 8.5
## 1335 10 1 15 1 buttery 0.1
## 1336 10 1 15 2 buttery 0.1
## 1337 10 1 16 1 buttery 7.0
## 1338 10 1 16 2 buttery 7.8
## 1339 10 1 19 1 buttery 1.6
## 1340 10 1 19 2 buttery 3.2
## 1341 10 1 31 1 buttery 0.0
## 1342 10 1 31 2 buttery 0.0
## 1343 10 1 51 1 buttery 2.2
## 1344 10 1 51 2 buttery 0.8
## 1345 10 1 52 1 buttery 0.0
## 1346 10 1 52 2 buttery 0.0
## 1347 10 1 63 1 buttery 0.0
## 1348 10 1 63 2 buttery 0.0
## 1349 10 1 78 1 buttery 0.8
## 1350 10 1 78 2 buttery 0.0
## 1351 10 1 86 1 buttery 0.0
## 1352 10 1 86 2 buttery 0.0
## 1353 10 2 10 1 buttery 6.8
## 1354 10 2 10 2 buttery 6.5
## 1355 10 2 15 1 buttery 1.4
## 1356 10 2 15 2 buttery 0.1
## 1357 10 2 16 1 buttery 6.0
## 1358 10 2 16 2 buttery 3.5
## 1359 10 2 19 1 buttery 0.0
## 1360 10 2 19 2 buttery 0.0
## 1361 10 2 31 1 buttery 0.0
## 1362 10 2 31 2 buttery 0.0
## 1363 10 2 51 1 buttery 5.3
## 1364 10 2 51 2 buttery 5.7
## 1365 10 2 52 1 buttery 0.0
## 1366 10 2 52 2 buttery 0.0
## 1367 10 2 63 1 buttery 0.6
## 1368 10 2 63 2 buttery 0.0
## 1369 10 2 78 1 buttery 0.0
## 1370 10 2 78 2 buttery 0.0
## 1371 10 2 86 1 buttery 0.0
## 1372 10 2 86 2 buttery 0.0
## 1373 10 3 10 1 buttery 9.2
## 1374 10 3 10 2 buttery 5.4
## 1375 10 3 15 1 buttery 0.4
## 1376 10 3 15 2 buttery 3.3
## 1377 10 3 16 1 buttery 1.4
## 1378 10 3 16 2 buttery 0.7
## 1379 10 3 19 1 buttery 4.0
## 1380 10 3 19 2 buttery 0.0
## 1381 10 3 31 1 buttery 0.0
## 1382 10 3 31 2 buttery 0.0
## 1383 10 3 51 1 buttery 3.2
## 1384 10 3 51 2 buttery 2.4
## 1385 10 3 52 1 buttery 0.8
## 1386 10 3 52 2 buttery 0.0
## 1387 10 3 63 1 buttery 0.0
## 1388 10 3 63 2 buttery 0.0
## 1389 10 3 78 1 buttery 0.0
## 1390 10 3 78 2 buttery 0.0
## 1391 10 3 86 1 buttery 0.0
## 1392 10 3 86 2 buttery 0.0
## 1393 1 1 3 1 grassy 0.0
## 1394 1 1 3 2 grassy 0.0
## 1395 1 1 10 1 grassy 0.0
## 1396 1 1 10 2 grassy 2.9
## 1397 1 1 15 1 grassy 0.0
## 1398 1 1 15 2 grassy 3.6
## 1399 1 1 16 1 grassy 0.4
## 1400 1 1 16 2 grassy 0.3
## 1401 1 1 19 1 grassy 0.0
## 1402 1 1 19 2 grassy 3.1
## 1403 1 1 31 1 grassy 0.0
## 1404 1 1 31 2 grassy 0.0
## 1405 1 1 51 1 grassy 0.1
## 1406 1 1 51 2 grassy 3.6
## 1407 1 1 52 1 grassy 1.7
## 1408 1 1 52 2 grassy 0.3
## 1409 1 1 63 1 grassy 0.0
## 1410 1 1 63 2 grassy 0.0
## 1411 1 1 78 1 grassy 0.0
## 1412 1 1 78 2 grassy 3.0
## 1413 1 1 79 1 grassy 0.0
## 1414 1 1 79 2 grassy 0.0
## 1415 1 1 86 1 grassy 0.0
## 1416 1 1 86 2 grassy 2.7
## 1417 1 2 3 1 grassy 0.0
## 1418 1 2 3 2 grassy 0.0
## 1419 1 2 10 1 grassy 3.3
## 1420 1 2 10 2 grassy 2.5
## 1421 1 2 15 1 grassy 0.3
## 1422 1 2 15 2 grassy 2.7
## 1423 1 2 16 1 grassy 0.8
## 1424 1 2 16 2 grassy 0.2
## 1425 1 2 19 1 grassy 1.3
## 1426 1 2 19 2 grassy 0.0
## 1427 1 2 31 1 grassy 0.0
## 1428 1 2 31 2 grassy 0.0
## 1429 1 2 51 1 grassy 4.3
## 1430 1 2 51 2 grassy 2.5
## 1431 1 2 52 1 grassy 0.0
## 1432 1 2 52 2 grassy 0.4
## 1433 1 2 63 1 grassy 0.0
## 1434 1 2 63 2 grassy 0.0
## 1435 1 2 78 1 grassy 1.2
## 1436 1 2 78 2 grassy 3.8
## 1437 1 2 79 1 grassy 0.6
## 1438 1 2 79 2 grassy 0.0
## 1439 1 2 86 1 grassy 0.0
## 1440 1 2 86 2 grassy 0.0
## 1441 1 3 3 1 grassy 0.0
## 1442 1 3 3 2 grassy 0.6
## 1443 1 3 10 1 grassy 0.0
## 1444 1 3 10 2 grassy 0.8
## 1445 1 3 15 1 grassy 0.5
## 1446 1 3 15 2 grassy 0.5
## 1447 1 3 16 1 grassy 0.6
## 1448 1 3 16 2 grassy 0.5
## 1449 1 3 19 1 grassy 5.2
## 1450 1 3 19 2 grassy 0.0
## 1451 1 3 31 1 grassy 0.3
## 1452 1 3 31 2 grassy 0.0
## 1453 1 3 51 1 grassy 0.0
## 1454 1 3 51 2 grassy 2.0
## 1455 1 3 52 1 grassy 0.0
## 1456 1 3 52 2 grassy 0.0
## 1457 1 3 63 1 grassy 1.9
## 1458 1 3 63 2 grassy 0.0
## 1459 1 3 78 1 grassy 0.0
## 1460 1 3 78 2 grassy 3.7
## 1461 1 3 79 1 grassy 1.6
## 1462 1 3 79 2 grassy 0.0
## 1463 1 3 86 1 grassy 1.5
## 1464 1 3 86 2 grassy 2.5
## 1465 2 1 3 1 grassy 0.1
## 1466 2 1 3 2 grassy 2.0
## 1467 2 1 10 1 grassy 3.1
## 1468 2 1 10 2 grassy 0.0
## 1469 2 1 15 1 grassy 0.1
## 1470 2 1 15 2 grassy 0.5
## 1471 2 1 16 1 grassy 0.0
## 1472 2 1 16 2 grassy 0.7
## 1473 2 1 19 1 grassy 0.0
## 1474 2 1 19 2 grassy 0.0
## 1475 2 1 31 1 grassy 0.0
## 1476 2 1 31 2 grassy 1.2
## 1477 2 1 51 1 grassy 1.5
## 1478 2 1 51 2 grassy 0.1
## 1479 2 1 52 1 grassy 2.8
## 1480 2 1 52 2 grassy 2.1
## 1481 2 1 63 1 grassy 0.0
## 1482 2 1 63 2 grassy 0.0
## 1483 2 1 78 1 grassy 1.3
## 1484 2 1 78 2 grassy 0.5
## 1485 2 1 79 1 grassy 2.3
## 1486 2 1 79 2 grassy 2.8
## 1487 2 1 86 1 grassy 0.0
## 1488 2 1 86 2 grassy 3.0
## 1489 2 2 3 1 grassy 0.3
## 1490 2 2 3 2 grassy 1.6
## 1491 2 2 10 1 grassy 1.6
## 1492 2 2 10 2 grassy 0.0
## 1493 2 2 15 1 grassy 0.7
## 1494 2 2 15 2 grassy 0.4
## 1495 2 2 16 1 grassy 0.7
## 1496 2 2 16 2 grassy 0.7
## 1497 2 2 19 1 grassy 1.9
## 1498 2 2 19 2 grassy 2.7
## 1499 2 2 31 1 grassy 0.0
## 1500 2 2 31 2 grassy 0.0
## 1501 2 2 51 1 grassy 1.2
## 1502 2 2 51 2 grassy 0.0
## 1503 2 2 52 1 grassy 0.4
## 1504 2 2 52 2 grassy 0.0
## 1505 2 2 63 1 grassy 0.0
## 1506 2 2 63 2 grassy 0.2
## 1507 2 2 78 1 grassy 5.1
## 1508 2 2 78 2 grassy 1.3
## 1509 2 2 79 1 grassy 2.1
## 1510 2 2 79 2 grassy 0.5
## 1511 2 2 86 1 grassy 1.4
## 1512 2 2 86 2 grassy 0.0
## 1513 2 3 3 1 grassy 0.7
## 1514 2 3 3 2 grassy 0.0
## 1515 2 3 10 1 grassy 2.1
## 1516 2 3 10 2 grassy 0.0
## 1517 2 3 15 1 grassy 3.5
## 1518 2 3 15 2 grassy 1.6
## 1519 2 3 16 1 grassy 1.3
## 1520 2 3 16 2 grassy 1.2
## 1521 2 3 19 1 grassy 5.8
## 1522 2 3 19 2 grassy 10.5
## 1523 2 3 31 1 grassy 0.0
## 1524 2 3 31 2 grassy 0.0
## 1525 2 3 51 1 grassy 0.0
## 1526 2 3 51 2 grassy 4.4
## 1527 2 3 52 1 grassy 0.8
## 1528 2 3 52 2 grassy 1.6
## 1529 2 3 63 1 grassy 0.0
## 1530 2 3 63 2 grassy 0.6
## 1531 2 3 78 1 grassy 0.0
## 1532 2 3 78 2 grassy 1.5
## 1533 2 3 79 1 grassy 0.0
## 1534 2 3 79 2 grassy 0.0
## 1535 2 3 86 1 grassy 2.6
## 1536 2 3 86 2 grassy 0.0
## 1537 3 1 3 1 grassy 0.0
## 1538 3 1 3 2 grassy 0.0
## 1539 3 1 10 1 grassy 0.0
## 1540 3 1 10 2 grassy 0.0
## 1541 3 1 15 1 grassy 0.2
## 1542 3 1 15 2 grassy 0.1
## 1543 3 1 16 1 grassy 3.0
## 1544 3 1 16 2 grassy 0.3
## 1545 3 1 19 1 grassy 3.6
## 1546 3 1 19 2 grassy 0.0
## 1547 3 1 31 1 grassy 0.0
## 1548 3 1 31 2 grassy 0.0
## 1549 3 1 51 1 grassy 0.2
## 1550 3 1 51 2 grassy 1.1
## 1551 3 1 52 1 grassy 4.1
## 1552 3 1 52 2 grassy 0.9
## 1553 3 1 63 1 grassy 0.0
## 1554 3 1 63 2 grassy 0.0
## 1555 3 1 78 1 grassy 0.0
## 1556 3 1 78 2 grassy 0.0
## 1557 3 1 79 1 grassy 0.0
## 1558 3 1 79 2 grassy 1.1
## 1559 3 1 86 1 grassy 2.0
## 1560 3 1 86 2 grassy 3.0
## 1561 3 2 3 1 grassy 0.0
## 1562 3 2 3 2 grassy 0.0
## 1563 3 2 10 1 grassy 0.1
## 1564 3 2 10 2 grassy 0.0
## 1565 3 2 15 1 grassy 0.0
## 1566 3 2 15 2 grassy 0.0
## 1567 3 2 16 1 grassy 0.0
## 1568 3 2 16 2 grassy 0.9
## 1569 3 2 19 1 grassy 0.0
## 1570 3 2 19 2 grassy 0.0
## 1571 3 2 31 1 grassy 0.0
## 1572 3 2 31 2 grassy 0.0
## 1573 3 2 51 1 grassy 0.0
## 1574 3 2 51 2 grassy 2.9
## 1575 3 2 52 1 grassy 1.8
## 1576 3 2 52 2 grassy 3.9
## 1577 3 2 63 1 grassy 0.0
## 1578 3 2 63 2 grassy 0.0
## 1579 3 2 78 1 grassy 0.0
## 1580 3 2 78 2 grassy 0.8
## 1581 3 2 79 1 grassy 0.0
## 1582 3 2 79 2 grassy 0.0
## 1583 3 2 86 1 grassy 4.8
## 1584 3 2 86 2 grassy 2.2
## 1585 3 3 3 1 grassy 0.0
## 1586 3 3 3 2 grassy 0.0
## 1587 3 3 10 1 grassy 0.0
## 1588 3 3 10 2 grassy 0.0
## 1589 3 3 15 1 grassy 0.3
## 1590 3 3 15 2 grassy 0.3
## 1591 3 3 16 1 grassy 0.5
## 1592 3 3 16 2 grassy 2.5
## 1593 3 3 19 1 grassy 1.0
## 1594 3 3 19 2 grassy 2.9
## 1595 3 3 31 1 grassy 0.0
## 1596 3 3 31 2 grassy 0.0
## 1597 3 3 51 1 grassy 0.0
## 1598 3 3 51 2 grassy 0.0
## 1599 3 3 52 1 grassy 2.0
## 1600 3 3 52 2 grassy 0.9
## 1601 3 3 63 1 grassy 0.0
## 1602 3 3 63 2 grassy 0.0
## 1603 3 3 78 1 grassy 1.8
## 1604 3 3 78 2 grassy 0.0
## 1605 3 3 79 1 grassy 0.0
## 1606 3 3 79 2 grassy 0.0
## 1607 3 3 86 1 grassy 2.1
## 1608 3 3 86 2 grassy 2.7
## 1609 4 1 3 1 grassy 0.0
## 1610 4 1 3 2 grassy 0.0
## 1611 4 1 10 1 grassy 0.0
## 1612 4 1 10 2 grassy 0.0
## 1613 4 1 15 1 grassy 0.4
## 1614 4 1 15 2 grassy 0.6
## 1615 4 1 16 1 grassy 1.2
## 1616 4 1 16 2 grassy 0.4
## 1617 4 1 19 1 grassy 0.0
## 1618 4 1 19 2 grassy 11.1
## 1619 4 1 31 1 grassy 0.0
## 1620 4 1 31 2 grassy 0.0
## 1621 4 1 51 1 grassy 0.7
## 1622 4 1 51 2 grassy 2.1
## 1623 4 1 52 1 grassy 0.2
## 1624 4 1 52 2 grassy 1.8
## 1625 4 1 63 1 grassy 0.0
## 1626 4 1 63 2 grassy 0.0
## 1627 4 1 78 1 grassy 1.2
## 1628 4 1 78 2 grassy 2.3
## 1629 4 1 79 1 grassy 0.0
## 1630 4 1 79 2 grassy 0.0
## 1631 4 1 86 1 grassy 0.0
## 1632 4 1 86 2 grassy 2.6
## 1633 4 2 3 1 grassy 0.0
## 1634 4 2 3 2 grassy 0.0
## 1635 4 2 10 1 grassy 0.0
## 1636 4 2 10 2 grassy 0.0
## 1637 4 2 15 1 grassy 0.0
## 1638 4 2 15 2 grassy 0.0
## 1639 4 2 16 1 grassy 2.1
## 1640 4 2 16 2 grassy 1.1
## 1641 4 2 19 1 grassy 0.0
## 1642 4 2 19 2 grassy 1.3
## 1643 4 2 31 1 grassy 1.0
## 1644 4 2 31 2 grassy 0.0
## 1645 4 2 51 1 grassy 3.3
## 1646 4 2 51 2 grassy 0.7
## 1647 4 2 52 1 grassy 2.4
## 1648 4 2 52 2 grassy 2.0
## 1649 4 2 63 1 grassy 0.0
## 1650 4 2 63 2 grassy 0.0
## 1651 4 2 78 1 grassy 0.0
## 1652 4 2 78 2 grassy 0.0
## 1653 4 2 79 1 grassy 0.0
## 1654 4 2 79 2 grassy 0.0
## 1655 4 2 86 1 grassy 1.5
## 1656 4 2 86 2 grassy 0.0
## 1657 4 3 3 1 grassy 0.4
## 1658 4 3 3 2 grassy 0.0
## 1659 4 3 10 1 grassy 0.0
## 1660 4 3 10 2 grassy 0.0
## 1661 4 3 15 1 grassy 0.3
## 1662 4 3 15 2 grassy 0.1
## 1663 4 3 16 1 grassy 2.2
## 1664 4 3 16 2 grassy 2.8
## 1665 4 3 19 1 grassy 0.0
## 1666 4 3 19 2 grassy 0.0
## 1667 4 3 31 1 grassy 0.0
## 1668 4 3 31 2 grassy 0.0
## 1669 4 3 51 1 grassy 0.1
## 1670 4 3 51 2 grassy 2.7
## 1671 4 3 52 1 grassy 0.9
## 1672 4 3 52 2 grassy 1.4
## 1673 4 3 63 1 grassy 0.0
## 1674 4 3 63 2 grassy 0.0
## 1675 4 3 78 1 grassy 0.0
## 1676 4 3 78 2 grassy 1.1
## 1677 4 3 79 1 grassy 0.0
## 1678 4 3 79 2 grassy 0.0
## 1679 4 3 86 1 grassy 1.4
## 1680 4 3 86 2 grassy 0.0
## 1681 5 1 3 1 grassy 0.0
## 1682 5 1 3 2 grassy 0.0
## 1683 5 1 10 1 grassy 1.5
## 1684 5 1 10 2 grassy 2.6
## 1685 5 1 15 1 grassy 0.3
## 1686 5 1 15 2 grassy 0.5
## 1687 5 1 16 1 grassy 2.8
## 1688 5 1 16 2 grassy 1.4
## 1689 5 1 19 1 grassy 1.7
## 1690 5 1 19 2 grassy 3.3
## 1691 5 1 31 1 grassy 0.4
## 1692 5 1 31 2 grassy 0.0
## 1693 5 1 51 1 grassy 0.2
## 1694 5 1 51 2 grassy 1.0
## 1695 5 1 52 1 grassy 0.0
## 1696 5 1 52 2 grassy 0.3
## 1697 5 1 63 1 grassy 0.0
## 1698 5 1 63 2 grassy 0.0
## 1699 5 1 78 1 grassy 1.3
## 1700 5 1 78 2 grassy 0.0
## 1701 5 1 79 1 grassy 0.0
## 1702 5 1 79 2 grassy 0.0
## 1703 5 1 86 1 grassy 0.0
## 1704 5 1 86 2 grassy 1.2
## 1705 5 2 3 1 grassy 0.0
## 1706 5 2 3 2 grassy 0.0
## 1707 5 2 10 1 grassy 0.0
## 1708 5 2 10 2 grassy 0.0
## 1709 5 2 15 1 grassy 0.2
## 1710 5 2 15 2 grassy 0.4
## 1711 5 2 16 1 grassy 0.1
## 1712 5 2 16 2 grassy 0.2
## 1713 5 2 19 1 grassy 0.0
## 1714 5 2 19 2 grassy 0.0
## 1715 5 2 31 1 grassy 0.5
## 1716 5 2 31 2 grassy 0.0
## 1717 5 2 51 1 grassy 0.7
## 1718 5 2 51 2 grassy 2.2
## 1719 5 2 52 1 grassy 7.1
## 1720 5 2 52 2 grassy 0.2
## 1721 5 2 63 1 grassy 0.0
## 1722 5 2 63 2 grassy 0.0
## 1723 5 2 78 1 grassy 0.9
## 1724 5 2 78 2 grassy 0.0
## 1725 5 2 79 1 grassy 0.0
## 1726 5 2 79 2 grassy 0.0
## 1727 5 2 86 1 grassy 0.0
## 1728 5 2 86 2 grassy 1.5
## 1729 5 3 3 1 grassy 0.0
## 1730 5 3 3 2 grassy 0.0
## 1731 5 3 10 1 grassy 0.0
## 1732 5 3 10 2 grassy 0.0
## 1733 5 3 15 1 grassy NA
## 1734 5 3 15 2 grassy 0.4
## 1735 5 3 16 1 grassy 0.5
## 1736 5 3 16 2 grassy 0.0
## 1737 5 3 19 1 grassy 0.0
## 1738 5 3 19 2 grassy 4.2
## 1739 5 3 31 1 grassy 0.0
## 1740 5 3 31 2 grassy 0.3
## 1741 5 3 51 1 grassy 2.5
## 1742 5 3 51 2 grassy 2.3
## 1743 5 3 52 1 grassy 0.8
## 1744 5 3 52 2 grassy 0.0
## 1745 5 3 63 1 grassy 0.0
## 1746 5 3 63 2 grassy 0.0
## 1747 5 3 78 1 grassy 1.6
## 1748 5 3 78 2 grassy 0.0
## 1749 5 3 79 1 grassy 0.0
## 1750 5 3 79 2 grassy 0.0
## 1751 5 3 86 1 grassy 0.0
## 1752 5 3 86 2 grassy 0.0
## 1753 6 1 3 1 grassy 0.0
## 1754 6 1 3 2 grassy 0.0
## 1755 6 1 10 1 grassy 0.0
## 1756 6 1 10 2 grassy 0.0
## 1757 6 1 15 1 grassy 0.2
## 1758 6 1 15 2 grassy 1.5
## 1759 6 1 16 1 grassy 0.3
## 1760 6 1 16 2 grassy 1.6
## 1761 6 1 19 1 grassy 0.0
## 1762 6 1 19 2 grassy 3.8
## 1763 6 1 31 1 grassy 0.0
## 1764 6 1 31 2 grassy 0.0
## 1765 6 1 51 1 grassy 1.7
## 1766 6 1 51 2 grassy 2.1
## 1767 6 1 52 1 grassy 0.0
## 1768 6 1 52 2 grassy 0.0
## 1769 6 1 63 1 grassy 0.0
## 1770 6 1 63 2 grassy 0.0
## 1771 6 1 78 1 grassy 0.0
## 1772 6 1 78 2 grassy 0.0
## 1773 6 1 79 1 grassy 0.0
## 1774 6 1 79 2 grassy 0.0
## 1775 6 1 86 1 grassy 0.0
## 1776 6 1 86 2 grassy 0.0
## 1777 6 2 3 1 grassy 0.0
## 1778 6 2 3 2 grassy 0.0
## 1779 6 2 10 1 grassy 0.0
## 1780 6 2 10 2 grassy 0.0
## 1781 6 2 15 1 grassy 0.3
## 1782 6 2 15 2 grassy 0.5
## 1783 6 2 16 1 grassy 6.6
## 1784 6 2 16 2 grassy 3.0
## 1785 6 2 19 1 grassy 2.9
## 1786 6 2 19 2 grassy 0.0
## 1787 6 2 31 1 grassy 0.0
## 1788 6 2 31 2 grassy 1.3
## 1789 6 2 51 1 grassy 0.0
## 1790 6 2 51 2 grassy 1.1
## 1791 6 2 52 1 grassy 1.8
## 1792 6 2 52 2 grassy 0.8
## 1793 6 2 63 1 grassy 0.0
## 1794 6 2 63 2 grassy 0.0
## 1795 6 2 78 1 grassy 0.0
## 1796 6 2 78 2 grassy 1.1
## 1797 6 2 79 1 grassy 0.0
## 1798 6 2 79 2 grassy 0.0
## 1799 6 2 86 1 grassy 2.7
## 1800 6 2 86 2 grassy 0.0
## 1801 6 3 3 1 grassy 0.0
## 1802 6 3 3 2 grassy 0.0
## 1803 6 3 10 1 grassy 0.0
## 1804 6 3 10 2 grassy 0.0
## 1805 6 3 15 1 grassy 0.1
## 1806 6 3 15 2 grassy 0.3
## 1807 6 3 16 1 grassy 3.1
## 1808 6 3 16 2 grassy 0.9
## 1809 6 3 19 1 grassy 0.0
## 1810 6 3 19 2 grassy 0.0
## 1811 6 3 31 1 grassy 0.0
## 1812 6 3 31 2 grassy 2.5
## 1813 6 3 51 1 grassy 0.9
## 1814 6 3 51 2 grassy 2.3
## 1815 6 3 52 1 grassy 0.8
## 1816 6 3 52 2 grassy 1.1
## 1817 6 3 63 1 grassy 0.0
## 1818 6 3 63 2 grassy 0.0
## 1819 6 3 78 1 grassy 1.5
## 1820 6 3 78 2 grassy 1.2
## 1821 6 3 79 1 grassy 0.5
## 1822 6 3 79 2 grassy 0.0
## 1823 6 3 86 1 grassy 0.0
## 1824 6 3 86 2 grassy 0.0
## 1825 7 1 3 1 grassy 0.0
## 1826 7 1 3 2 grassy 0.0
## 1827 7 1 10 1 grassy 0.0
## 1828 7 1 10 2 grassy 0.0
## 1829 7 1 15 1 grassy 0.0
## 1830 7 1 15 2 grassy 0.0
## 1831 7 1 16 1 grassy 0.4
## 1832 7 1 16 2 grassy 0.1
## 1833 7 1 19 1 grassy 0.0
## 1834 7 1 19 2 grassy 0.0
## 1835 7 1 31 1 grassy 0.0
## 1836 7 1 31 2 grassy 0.0
## 1837 7 1 51 1 grassy 0.1
## 1838 7 1 51 2 grassy 0.0
## 1839 7 1 52 1 grassy 1.7
## 1840 7 1 52 2 grassy 1.6
## 1841 7 1 63 1 grassy 0.0
## 1842 7 1 63 2 grassy 0.0
## 1843 7 1 78 1 grassy 0.0
## 1844 7 1 78 2 grassy 0.0
## 1845 7 1 79 1 grassy 0.0
## 1846 7 1 79 2 grassy 0.0
## 1847 7 1 86 1 grassy 0.0
## 1848 7 1 86 2 grassy 0.0
## 1849 7 2 3 1 grassy 0.0
## 1850 7 2 3 2 grassy 0.0
## 1851 7 2 10 1 grassy 0.0
## 1852 7 2 10 2 grassy 0.0
## 1853 7 2 15 1 grassy 0.2
## 1854 7 2 15 2 grassy 0.0
## 1855 7 2 16 1 grassy 0.0
## 1856 7 2 16 2 grassy 0.3
## 1857 7 2 19 1 grassy 0.0
## 1858 7 2 19 2 grassy 6.7
## 1859 7 2 31 1 grassy 0.0
## 1860 7 2 31 2 grassy 0.0
## 1861 7 2 51 1 grassy 0.0
## 1862 7 2 51 2 grassy 3.2
## 1863 7 2 52 1 grassy 1.5
## 1864 7 2 52 2 grassy 1.3
## 1865 7 2 63 1 grassy 0.0
## 1866 7 2 63 2 grassy 0.0
## 1867 7 2 78 1 grassy 0.0
## 1868 7 2 78 2 grassy 0.0
## 1869 7 2 79 1 grassy 0.0
## 1870 7 2 79 2 grassy 0.0
## 1871 7 2 86 1 grassy 0.0
## 1872 7 2 86 2 grassy 0.0
## 1873 7 3 3 1 grassy 0.0
## 1874 7 3 3 2 grassy 0.0
## 1875 7 3 10 1 grassy 0.0
## 1876 7 3 10 2 grassy 0.0
## 1877 7 3 15 1 grassy 0.0
## 1878 7 3 15 2 grassy 0.1
## 1879 7 3 16 1 grassy 0.0
## 1880 7 3 16 2 grassy 0.1
## 1881 7 3 19 1 grassy 0.0
## 1882 7 3 19 2 grassy 0.0
## 1883 7 3 31 1 grassy 0.0
## 1884 7 3 31 2 grassy 0.0
## 1885 7 3 51 1 grassy 2.9
## 1886 7 3 51 2 grassy 1.1
## 1887 7 3 52 1 grassy 1.1
## 1888 7 3 52 2 grassy 3.7
## 1889 7 3 63 1 grassy 0.0
## 1890 7 3 63 2 grassy 0.0
## 1891 7 3 78 1 grassy 0.0
## 1892 7 3 78 2 grassy 0.0
## 1893 7 3 79 1 grassy 0.0
## 1894 7 3 79 2 grassy 0.0
## 1895 7 3 86 1 grassy 2.8
## 1896 7 3 86 2 grassy 1.4
## 1897 8 1 3 1 grassy 1.3
## 1898 8 1 3 2 grassy 0.0
## 1899 8 1 10 1 grassy 0.0
## 1900 8 1 10 2 grassy 0.0
## 1901 8 1 15 1 grassy 0.1
## 1902 8 1 15 2 grassy 0.0
## 1903 8 1 16 1 grassy 1.8
## 1904 8 1 16 2 grassy 0.2
## 1905 8 1 19 1 grassy 0.0
## 1906 8 1 19 2 grassy 11.1
## 1907 8 1 31 1 grassy 0.0
## 1908 8 1 31 2 grassy 0.0
## 1909 8 1 51 1 grassy 0.7
## 1910 8 1 51 2 grassy 0.0
## 1911 8 1 52 1 grassy 0.0
## 1912 8 1 52 2 grassy 0.0
## 1913 8 1 63 1 grassy 0.0
## 1914 8 1 63 2 grassy 0.0
## 1915 8 1 78 1 grassy 0.0
## 1916 8 1 78 2 grassy 0.0
## 1917 8 1 79 1 grassy 0.0
## 1918 8 1 79 2 grassy 0.0
## 1919 8 1 86 1 grassy 0.0
## 1920 8 1 86 2 grassy 0.0
## 1921 8 2 3 1 grassy 0.0
## 1922 8 2 3 2 grassy 0.0
## 1923 8 2 10 1 grassy 0.0
## 1924 8 2 10 2 grassy 0.0
## 1925 8 2 15 1 grassy 0.3
## 1926 8 2 15 2 grassy 0.1
## 1927 8 2 16 1 grassy 1.4
## 1928 8 2 16 2 grassy 0.2
## 1929 8 2 19 1 grassy 0.0
## 1930 8 2 19 2 grassy 0.0
## 1931 8 2 31 1 grassy 0.0
## 1932 8 2 31 2 grassy 0.0
## 1933 8 2 51 1 grassy 2.0
## 1934 8 2 51 2 grassy 4.1
## 1935 8 2 52 1 grassy 0.0
## 1936 8 2 52 2 grassy 0.0
## 1937 8 2 63 1 grassy 0.0
## 1938 8 2 63 2 grassy 0.0
## 1939 8 2 78 1 grassy 0.0
## 1940 8 2 78 2 grassy 0.0
## 1941 8 2 79 1 grassy 0.0
## 1942 8 2 79 2 grassy 1.4
## 1943 8 2 86 1 grassy 0.0
## 1944 8 2 86 2 grassy 0.0
## 1945 8 3 3 1 grassy 0.0
## 1946 8 3 3 2 grassy 0.0
## 1947 8 3 10 1 grassy 0.0
## 1948 8 3 10 2 grassy 0.0
## 1949 8 3 15 1 grassy 0.0
## 1950 8 3 15 2 grassy 0.2
## 1951 8 3 16 1 grassy 0.5
## 1952 8 3 16 2 grassy 0.1
## 1953 8 3 19 1 grassy 0.0
## 1954 8 3 19 2 grassy 0.0
## 1955 8 3 31 1 grassy 0.0
## 1956 8 3 31 2 grassy 0.0
## 1957 8 3 51 1 grassy 1.9
## 1958 8 3 51 2 grassy 0.0
## 1959 8 3 52 1 grassy 0.0
## 1960 8 3 52 2 grassy 0.0
## 1961 8 3 63 1 grassy 0.0
## 1962 8 3 63 2 grassy 0.0
## 1963 8 3 78 1 grassy 0.0
## 1964 8 3 78 2 grassy 0.0
## 1965 8 3 79 1 grassy 0.0
## 1966 8 3 79 2 grassy 0.0
## 1967 8 3 86 1 grassy 0.0
## 1968 8 3 86 2 grassy 0.0
## 1969 9 1 3 1 grassy 0.0
## 1970 9 1 3 2 grassy 0.0
## 1971 9 1 10 1 grassy 0.0
## 1972 9 1 10 2 grassy 0.0
## 1973 9 1 15 1 grassy 0.1
## 1974 9 1 15 2 grassy 0.0
## 1975 9 1 16 1 grassy 0.1
## 1976 9 1 16 2 grassy 0.1
## 1977 9 1 19 1 grassy 0.0
## 1978 9 1 19 2 grassy 0.0
## 1979 9 1 51 1 grassy 1.7
## 1980 9 1 51 2 grassy 0.7
## 1981 9 1 52 1 grassy 0.0
## 1982 9 1 52 2 grassy 0.0
## 1983 9 1 63 1 grassy 0.0
## 1984 9 1 63 2 grassy 0.0
## 1985 9 1 78 1 grassy 0.0
## 1986 9 1 78 2 grassy 0.0
## 1987 9 1 79 1 grassy 0.0
## 1988 9 1 79 2 grassy 0.0
## 1989 9 2 3 1 grassy 0.0
## 1990 9 2 3 2 grassy 0.0
## 1991 9 2 10 1 grassy 0.0
## 1992 9 2 10 2 grassy 0.0
## 1993 9 2 15 1 grassy 0.2
## 1994 9 2 15 2 grassy 0.0
## 1995 9 2 16 1 grassy 0.5
## 1996 9 2 16 2 grassy 0.0
## 1997 9 2 19 1 grassy 0.0
## 1998 9 2 19 2 grassy 0.0
## 1999 9 2 51 1 grassy 0.0
## 2000 9 2 51 2 grassy 0.0
## 2001 9 2 52 1 grassy 0.0
## 2002 9 2 52 2 grassy 0.0
## 2003 9 2 63 1 grassy 0.0
## 2004 9 2 63 2 grassy 0.0
## 2005 9 2 78 1 grassy 0.0
## 2006 9 2 78 2 grassy 0.9
## 2007 9 2 79 1 grassy 0.0
## 2008 9 2 79 2 grassy 0.0
## 2009 9 3 3 1 grassy 0.0
## 2010 9 3 3 2 grassy 0.0
## 2011 9 3 10 1 grassy 0.0
## 2012 9 3 10 2 grassy 0.0
## 2013 9 3 15 1 grassy 0.1
## 2014 9 3 15 2 grassy 0.0
## 2015 9 3 16 1 grassy 0.7
## 2016 9 3 16 2 grassy 2.4
## 2017 9 3 19 1 grassy 0.0
## 2018 9 3 19 2 grassy 7.2
## 2019 9 3 51 1 grassy 1.9
## 2020 9 3 51 2 grassy 0.0
## 2021 9 3 52 1 grassy 0.0
## 2022 9 3 52 2 grassy 0.0
## 2023 9 3 63 1 grassy 0.0
## 2024 9 3 63 2 grassy 0.0
## 2025 9 3 78 1 grassy 0.0
## 2026 9 3 78 2 grassy 0.0
## 2027 9 3 79 1 grassy 0.0
## 2028 9 3 79 2 grassy 0.0
## 2029 10 1 10 1 grassy 1.6
## 2030 10 1 10 2 grassy 0.0
## 2031 10 1 15 1 grassy 0.1
## 2032 10 1 15 2 grassy 0.1
## 2033 10 1 16 1 grassy 0.0
## 2034 10 1 16 2 grassy 0.0
## 2035 10 1 19 1 grassy 0.0
## 2036 10 1 19 2 grassy 2.7
## 2037 10 1 31 1 grassy 0.0
## 2038 10 1 31 2 grassy 0.0
## 2039 10 1 51 1 grassy 1.4
## 2040 10 1 51 2 grassy 2.0
## 2041 10 1 52 1 grassy 0.0
## 2042 10 1 52 2 grassy 0.0
## 2043 10 1 63 1 grassy 0.0
## 2044 10 1 63 2 grassy 0.0
## 2045 10 1 78 1 grassy 0.0
## 2046 10 1 78 2 grassy 1.2
## 2047 10 1 86 1 grassy 0.0
## 2048 10 1 86 2 grassy 0.0
## 2049 10 2 10 1 grassy 2.0
## 2050 10 2 10 2 grassy 0.0
## 2051 10 2 15 1 grassy 0.3
## 2052 10 2 15 2 grassy 0.2
## 2053 10 2 16 1 grassy 1.2
## 2054 10 2 16 2 grassy 1.1
## 2055 10 2 19 1 grassy 1.7
## 2056 10 2 19 2 grassy 4.2
## 2057 10 2 31 1 grassy 0.0
## 2058 10 2 31 2 grassy 0.0
## 2059 10 2 51 1 grassy 0.8
## 2060 10 2 51 2 grassy 2.4
## 2061 10 2 52 1 grassy 0.0
## 2062 10 2 52 2 grassy 0.0
## 2063 10 2 63 1 grassy 0.0
## 2064 10 2 63 2 grassy 0.0
## 2065 10 2 78 1 grassy 0.0
## 2066 10 2 78 2 grassy 0.0
## 2067 10 2 86 1 grassy 0.0
## 2068 10 2 86 2 grassy 0.0
## 2069 10 3 10 1 grassy 0.0
## 2070 10 3 10 2 grassy 0.0
## 2071 10 3 15 1 grassy 0.1
## 2072 10 3 15 2 grassy 0.0
## 2073 10 3 16 1 grassy 1.0
## 2074 10 3 16 2 grassy 1.6
## 2075 10 3 19 1 grassy 0.0
## 2076 10 3 19 2 grassy 4.6
## 2077 10 3 31 1 grassy 0.0
## 2078 10 3 31 2 grassy 0.0
## 2079 10 3 51 1 grassy 0.0
## 2080 10 3 51 2 grassy 2.0
## 2081 10 3 52 1 grassy 0.2
## 2082 10 3 52 2 grassy 0.0
## 2083 10 3 63 1 grassy 0.0
## 2084 10 3 63 2 grassy 0.0
## 2085 10 3 78 1 grassy 0.9
## 2086 10 3 78 2 grassy 0.0
## 2087 10 3 86 1 grassy 0.0
## 2088 10 3 86 2 grassy 0.0
## 2089 1 1 3 1 rancid 0.0
## 2090 1 1 3 2 rancid 1.1
## 2091 1 1 10 1 rancid 0.0
## 2092 1 1 10 2 rancid 2.2
## 2093 1 1 15 1 rancid 1.1
## 2094 1 1 15 2 rancid 1.5
## 2095 1 1 16 1 rancid 0.1
## 2096 1 1 16 2 rancid 1.4
## 2097 1 1 19 1 rancid 4.9
## 2098 1 1 19 2 rancid 4.3
## 2099 1 1 31 1 rancid 2.5
## 2100 1 1 31 2 rancid 5.6
## 2101 1 1 51 1 rancid 1.4
## 2102 1 1 51 2 rancid 3.3
## 2103 1 1 52 1 rancid 8.5
## 2104 1 1 52 2 rancid 2.8
## 2105 1 1 63 1 rancid 1.1
## 2106 1 1 63 2 rancid 12.9
## 2107 1 1 78 1 rancid 1.1
## 2108 1 1 78 2 rancid 0.5
## 2109 1 1 79 1 rancid 2.0
## 2110 1 1 79 2 rancid 0.0
## 2111 1 1 86 1 rancid 7.9
## 2112 1 1 86 2 rancid 0.0
## 2113 1 2 3 1 rancid 3.9
## 2114 1 2 3 2 rancid 1.5
## 2115 1 2 10 1 rancid 0.0
## 2116 1 2 10 2 rancid 0.0
## 2117 1 2 15 1 rancid 2.0
## 2118 1 2 15 2 rancid 1.9
## 2119 1 2 16 1 rancid 0.2
## 2120 1 2 16 2 rancid 0.4
## 2121 1 2 19 1 rancid 0.0
## 2122 1 2 19 2 rancid 2.5
## 2123 1 2 31 1 rancid 7.0
## 2124 1 2 31 2 rancid 0.8
## 2125 1 2 51 1 rancid 3.3
## 2126 1 2 51 2 rancid 11.8
## 2127 1 2 52 1 rancid 0.0
## 2128 1 2 52 2 rancid 1.7
## 2129 1 2 63 1 rancid 0.0
## 2130 1 2 63 2 rancid 0.2
## 2131 1 2 78 1 rancid 2.0
## 2132 1 2 78 2 rancid 0.9
## 2133 1 2 79 1 rancid 0.0
## 2134 1 2 79 2 rancid 1.1
## 2135 1 2 86 1 rancid 0.0
## 2136 1 2 86 2 rancid 0.0
## 2137 1 3 3 1 rancid 1.1
## 2138 1 3 3 2 rancid 2.8
## 2139 1 3 10 1 rancid 0.0
## 2140 1 3 10 2 rancid 0.0
## 2141 1 3 15 1 rancid 2.8
## 2142 1 3 15 2 rancid 0.8
## 2143 1 3 16 1 rancid 1.4
## 2144 1 3 16 2 rancid 3.6
## 2145 1 3 19 1 rancid 4.4
## 2146 1 3 19 2 rancid 13.2
## 2147 1 3 31 1 rancid 6.6
## 2148 1 3 31 2 rancid 8.1
## 2149 1 3 51 1 rancid 2.1
## 2150 1 3 51 2 rancid 6.0
## 2151 1 3 52 1 rancid 1.2
## 2152 1 3 52 2 rancid 0.0
## 2153 1 3 63 1 rancid 0.0
## 2154 1 3 63 2 rancid 0.4
## 2155 1 3 78 1 rancid 3.6
## 2156 1 3 78 2 rancid 2.5
## 2157 1 3 79 1 rancid 0.9
## 2158 1 3 79 2 rancid 0.9
## 2159 1 3 86 1 rancid 0.0
## 2160 1 3 86 2 rancid 0.0
## 2161 2 1 3 1 rancid 5.8
## 2162 2 1 3 2 rancid 8.6
## 2163 2 1 10 1 rancid 6.2
## 2164 2 1 10 2 rancid 0.0
## 2165 2 1 15 1 rancid 3.9
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## 3332 8 2 78 2 painty 6.3
## 3333 8 2 79 1 painty 0.0
## 3334 8 2 79 2 painty NA
## 3335 8 2 86 1 painty 10.5
## 3336 8 2 86 2 painty 8.0
## 3337 8 3 3 1 painty 5.9
## 3338 8 3 3 2 painty 11.3
## 3339 8 3 10 1 painty 0.0
## 3340 8 3 10 2 painty 0.0
## 3341 8 3 15 1 painty 3.4
## 3342 8 3 15 2 painty 6.8
## 3343 8 3 16 1 painty 0.1
## 3344 8 3 16 2 painty 0.0
## 3345 8 3 19 1 painty 5.3
## 3346 8 3 19 2 painty 8.5
## 3347 8 3 31 1 painty 3.3
## 3348 8 3 31 2 painty 0.0
## 3349 8 3 51 1 painty 0.1
## 3350 8 3 51 2 painty 0.1
## 3351 8 3 52 1 painty 7.2
## 3352 8 3 52 2 painty 3.5
## 3353 8 3 63 1 painty 0.4
## 3354 8 3 63 2 painty 6.6
## 3355 8 3 78 1 painty 0.6
## 3356 8 3 78 2 painty 1.3
## 3357 8 3 79 1 painty 0.0
## 3358 8 3 79 2 painty 0.0
## 3359 8 3 86 1 painty 7.5
## 3360 8 3 86 2 painty 8.7
## 3361 9 1 3 1 painty 7.0
## 3362 9 1 3 2 painty 3.4
## 3363 9 1 10 1 painty 0.0
## 3364 9 1 10 2 painty 0.0
## 3365 9 1 15 1 painty 10.8
## 3366 9 1 15 2 painty 3.1
## 3367 9 1 16 1 painty 0.2
## 3368 9 1 16 2 painty 0.0
## 3369 9 1 19 1 painty 2.4
## 3370 9 1 19 2 painty 0.0
## 3371 9 1 51 1 painty 3.0
## 3372 9 1 51 2 painty 1.6
## 3373 9 1 52 1 painty 3.7
## 3374 9 1 52 2 painty 3.2
## 3375 9 1 63 1 painty 12.8
## 3376 9 1 63 2 painty 1.7
## 3377 9 1 78 1 painty 5.2
## 3378 9 1 78 2 painty 0.0
## 3379 9 1 79 1 painty 0.0
## 3380 9 1 79 2 painty 0.0
## 3381 9 2 3 1 painty 0.0
## 3382 9 2 3 2 painty 7.0
## 3383 9 2 10 1 painty 10.6
## 3384 9 2 10 2 painty 0.0
## 3385 9 2 15 1 painty 10.6
## 3386 9 2 15 2 painty 6.4
## 3387 9 2 16 1 painty 0.1
## 3388 9 2 16 2 painty 0.0
## 3389 9 2 19 1 painty 0.0
## 3390 9 2 19 2 painty 12.6
## 3391 9 2 51 1 painty 0.1
## 3392 9 2 51 2 painty 2.0
## 3393 9 2 52 1 painty 2.5
## 3394 9 2 52 2 painty 8.5
## 3395 9 2 63 1 painty 12.3
## 3396 9 2 63 2 painty 4.4
## 3397 9 2 78 1 painty 1.6
## 3398 9 2 78 2 painty 2.2
## 3399 9 2 79 1 painty 0.0
## 3400 9 2 79 2 painty 0.0
## 3401 9 3 3 1 painty 4.3
## 3402 9 3 3 2 painty 6.7
## 3403 9 3 10 1 painty 1.7
## 3404 9 3 10 2 painty 4.9
## 3405 9 3 15 1 painty 7.3
## 3406 9 3 15 2 painty 7.1
## 3407 9 3 16 1 painty 0.3
## 3408 9 3 16 2 painty 0.0
## 3409 9 3 19 1 painty 8.9
## 3410 9 3 19 2 painty 11.3
## 3411 9 3 51 1 painty 4.2
## 3412 9 3 51 2 painty 0.0
## 3413 9 3 52 1 painty 4.5
## 3414 9 3 52 2 painty 9.5
## 3415 9 3 63 1 painty 8.4
## 3416 9 3 63 2 painty 10.5
## 3417 9 3 78 1 painty 3.8
## 3418 9 3 78 2 painty 0.0
## 3419 9 3 79 1 painty 0.0
## 3420 9 3 79 2 painty 0.0
## 3421 10 1 10 1 painty 8.2
## 3422 10 1 10 2 painty 2.9
## 3423 10 1 15 1 painty 2.7
## 3424 10 1 15 2 painty 1.0
## 3425 10 1 16 1 painty 2.7
## 3426 10 1 16 2 painty 3.1
## 3427 10 1 19 1 painty 0.0
## 3428 10 1 19 2 painty 3.6
## 3429 10 1 31 1 painty 12.6
## 3430 10 1 31 2 painty 11.4
## 3431 10 1 51 1 painty 1.4
## 3432 10 1 51 2 painty 0.0
## 3433 10 1 52 1 painty 10.8
## 3434 10 1 52 2 painty 11.0
## 3435 10 1 63 1 painty 1.8
## 3436 10 1 63 2 painty 2.6
## 3437 10 1 78 1 painty 1.3
## 3438 10 1 78 2 painty 6.2
## 3439 10 1 86 1 painty 11.6
## 3440 10 1 86 2 painty 13.1
## 3441 10 2 10 1 painty 3.6
## 3442 10 2 10 2 painty 0.0
## 3443 10 2 15 1 painty 0.7
## 3444 10 2 15 2 painty 3.2
## 3445 10 2 16 1 painty 0.6
## 3446 10 2 16 2 painty 2.1
## 3447 10 2 19 1 painty 10.3
## 3448 10 2 19 2 painty 10.9
## 3449 10 2 31 1 painty 9.1
## 3450 10 2 31 2 painty 10.2
## 3451 10 2 51 1 painty 2.5
## 3452 10 2 51 2 painty 5.4
## 3453 10 2 52 1 painty 7.4
## 3454 10 2 52 2 painty 6.4
## 3455 10 2 63 1 painty 9.5
## 3456 10 2 63 2 painty 10.7
## 3457 10 2 78 1 painty 4.1
## 3458 10 2 78 2 painty 2.4
## 3459 10 2 86 1 painty 12.7
## 3460 10 2 86 2 painty 11.6
## 3461 10 3 10 1 painty 0.0
## 3462 10 3 10 2 painty 1.9
## 3463 10 3 15 1 painty 0.2
## 3464 10 3 15 2 painty 0.3
## 3465 10 3 16 1 painty 4.4
## 3466 10 3 16 2 painty 1.3
## 3467 10 3 19 1 painty 0.0
## 3468 10 3 19 2 painty 11.8
## 3469 10 3 31 1 painty 11.4
## 3470 10 3 31 2 painty 0.0
## 3471 10 3 51 1 painty 3.3
## 3472 10 3 51 2 painty 0.0
## 3473 10 3 52 1 painty 9.0
## 3474 10 3 52 2 painty 9.3
## 3475 10 3 63 1 painty 3.3
## 3476 10 3 63 2 painty 7.3
## 3477 10 3 78 1 painty 1.3
## 3478 10 3 78 2 painty 1.4
## 3479 10 3 86 1 painty 10.5
## 3480 10 3 86 2 painty 9.4head(french_fries,3)## time treatment subject rep potato buttery grassy rancid painty
## 61 1 1 3 1 2.9 0.0 0 0.0 5.5
## 25 1 1 3 2 14.0 0.0 0 1.1 0.0
## 62 1 1 10 1 11.0 6.4 0 0.0 0.0head(frymelt,3)## time treatment subject rep variable value
## 1 1 1 3 1 potato 2.9
## 2 1 1 3 2 potato 14.0
## 3 1 1 10 1 potato 11.0# frycast <-cast(frymelt,...~rep)
#anothe example
#create data frame in wide format
df <- data.frame(team=c('A', 'B', 'C', 'D'),
points=c(88, 91, 99, 94),
assists=c(12, 17, 24, 28),
rebounds=c(22, 28, 30, 31))
head(df)## team points assists rebounds
## 1 A 88 12 22
## 2 B 91 17 28
## 3 C 99 24 30
## 4 D 94 28 31long_df <- melt(df,id.vars = "team")
long_df ## team variable value
## 1 A points 88
## 2 B points 91
## 3 C points 99
## 4 D points 94
## 5 A assists 12
## 6 B assists 17
## 7 C assists 24
## 8 D assists 28
## 9 A rebounds 22
## 10 B rebounds 28
## 11 C rebounds 30
## 12 D rebounds 31# Scatter Plots for continuous data
library(dplyr)
library(ggplot2)
starwars## # A tibble: 87 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Luke Sk… 172 77 blond fair blue 19 male mascu…
## 2 C-3PO 167 75 <NA> gold yellow 112 none mascu…
## 3 R2-D2 96 32 <NA> white, bl… red 33 none mascu…
## 4 Darth V… 202 136 none white yellow 41.9 male mascu…
## 5 Leia Or… 150 49 brown light brown 19 fema… femin…
## 6 Owen La… 178 120 brown, gr… light blue 52 male mascu…
## 7 Beru Wh… 165 75 brown light blue 47 fema… femin…
## 8 R5-D4 97 32 <NA> white, red red NA none mascu…
## 9 Biggs D… 183 84 black light brown 24 male mascu…
## 10 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male mascu…
## # ℹ 77 more rows
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>humans <-filter(starwars, species=="Human")
head(humans) ## # A tibble: 6 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Luke Sky… 172 77 blond fair blue 19 male mascu…
## 2 Darth Va… 202 136 none white yellow 41.9 male mascu…
## 3 Leia Org… 150 49 brown light brown 19 fema… femin…
## 4 Owen Lars 178 120 brown, gr… light blue 52 male mascu…
## 5 Beru Whi… 165 75 brown light blue 47 fema… femin…
## 6 Biggs Da… 183 84 black light brown 24 male mascu…
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>humans <-humans[!is.na(humans$mass),]
head(humans)## # A tibble: 6 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Luke Sky… 172 77 blond fair blue 19 male mascu…
## 2 Darth Va… 202 136 none white yellow 41.9 male mascu…
## 3 Leia Org… 150 49 brown light brown 19 fema… femin…
## 4 Owen Lars 178 120 brown, gr… light blue 52 male mascu…
## 5 Beru Whi… 165 75 brown light blue 47 fema… femin…
## 6 Biggs Da… 183 84 black light brown 24 male mascu…
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>is.na(humans$mass)## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEggplot(humans,aes(x=mass,y=height))+geom_point(size=4)# some of the dots are overlapping which is called overplotting
ggplot(humans, aes(x=mass,y=height))+
geom_point(size=4,alpha=0.4,position="jitter")#points are moving away
# or simply use
ggplot(humans, aes(x=mass,y=height))+
geom_jitter(size=4,alpha=0.4)#points are moving away
# BMI calculation =>mass(kg)/height(m)^2
humans <-mutate(humans, BMI=mass/(height/100)^2) # add a new column by mutate
humans## # A tibble: 22 × 15
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Luke Sk… 172 77 blond fair blue 19 male mascu…
## 2 Darth V… 202 136 none white yellow 41.9 male mascu…
## 3 Leia Or… 150 49 brown light brown 19 fema… femin…
## 4 Owen La… 178 120 brown, gr… light blue 52 male mascu…
## 5 Beru Wh… 165 75 brown light blue 47 fema… femin…
## 6 Biggs D… 183 84 black light brown 24 male mascu…
## 7 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male mascu…
## 8 Anakin … 188 84 blond fair blue 41.9 male mascu…
## 9 Han Solo 180 80 brown fair brown 29 male mascu…
## 10 Wedge A… 170 77 brown fair hazel 21 male mascu…
## # ℹ 12 more rows
## # ℹ 6 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>, BMI <dbl>ggplot(humans, aes(x=mass,y=height))+
geom_point(aes(color=BMI), size=4)
ggplot(humans, aes(x=mass,y=height))+
geom_point(aes(color=BMI), size=4)+scale_color_gradient(low="green", high="red")
ggplot(humans, aes(x=mass,y=height))+
geom_point(aes(color=BMI), size=4)+
scale_color_gradient2(low="green",mid="blue",midpoint=22, high="red")
# this gives a heat map but gradient increase in reverse order, so rev in example
ggplot(humans, aes(x=mass,y=height))+
geom_point(aes(color=BMI), size=4)+
scale_color_gradientn(colors=heat.colors(3))
ggplot(humans, aes(x=mass,y=height))+
geom_point(aes(color=BMI), size=4)+
scale_color_gradientn(colors=rev(heat.colors(3)))
#create a custome palette
mypalette <-c("royalblue","forestgreen","gold","darkorange","firebrick")
mypalette## [1] "royalblue" "forestgreen" "gold" "darkorange" "firebrick"ggplot(humans, aes(x=mass,y=height))+
geom_point(aes(color=BMI), size=4)+
scale_color_gradientn(colors=mypalette)
# Scatter Plots for Discrete data
humans_BMI<-cut(humans$BMI, breaks=c(0,18.5,25,30,35,Inf), labels=c("Underwieght","Normal","Overwieght","Obese","Extremely Obese"), right=FALSE)
humans_BMI## [1] Overwieght Obese Normal Extremely Obese
## [5] Overwieght Overwieght Normal Normal
## [9] Normal Overwieght Obese Overwieght
## [13] Normal Overwieght Overwieght Normal
## [17] Normal Normal Normal Normal
## [21] Normal Underwieght
## Levels: Underwieght Normal Overwieght Obese Extremely Obeseggplot(humans, aes(x=mass,y=height))+
geom_point(aes(color=humans_BMI), size=4)
ggplot(humans, aes(x=mass,y=height))+
geom_point(aes(color=humans_BMI), size=4)+scale_color_manual(values=mypalette, name="BMI")
ggplot(humans, aes(x=mass,y=height))+
geom_point(aes(color=humans_BMI), size=10, alpha=0.4)+ scale_color_manual(values=mypalette,name="BMI")
# Name are ovelapped
ggplot(humans, aes(x=mass,y=height))+
geom_point(aes(color=humans_BMI), size=10, alpha=0.4)+ scale_color_manual(values=mypalette,name="BMI")+geom_text(aes(label=name))
# Nmes not cluttered
library(ggrepel)
ggplot(humans,
aes(x=mass,y=height))+
geom_point(aes(color=humans_BMI), size=10, alpha=0.4)+
scale_color_manual(values=mypalette,name="BMI")+
geom_text_repel(aes(label=name))
#library(ggplot2)
#library(dplyr)
#library(ggrepel)
#class_info <-summarise(group_by(mpg,class), n=,hwy=mean(hwy))## Statistical Transformations
ggplot(mpg, aes(x=cty,y=hwy))+
geom_point()
ggplot(mpg, aes(x=cty,y=hwy))+
geom_point(stat="smooth")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(mpg, aes(x=cty,y=hwy))+
geom_smooth()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# same result as before
ggplot(mpg, aes(x=cty,y=hwy))+
stat_smooth()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(mpg, aes(x=cty))+stat_count()
ggplot(mpg, aes(x=cty,y=hwy))+stat_sum()
#Interesting Stats
ggplot(mpg, aes(x=cty,y=hwy))+
geom_smooth()+geom_point()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(mpg, aes(x=class, y=hwy))+geom_point()
# has repeated values
ggplot(mpg, aes(x=class, y=hwy))+geom_point(alpha=.3)
# no repeated values
ggplot(mpg, aes(x=class, y=hwy))+stat_unique(alpha=.3) 
#https://www.kaggle.com/code/upadorprofzs/basic-visualization-techniques
#https://www.kaggle.com/code/janiobachmann/statistical-analysis-a-frequentist-approach
#https://www.kaggle.com/code/hiteshp/head-start-for-data-scientist#https://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html#google_vignette
library(ggplot2)
theme_set(theme_bw())
# Data Prep
library(ggplot2)
data("mtcars") # load data
mtcars$`car name` <- rownames(mtcars) # create new column for car names
mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2) # compute normalized mpg
mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, "below", "above") # above / below avg flag
mtcars <- mtcars[order(mtcars$mpg_z), ] # sort
mtcars$`car name` <- factor(mtcars$`car name`, levels = mtcars$`car name`) # convert to factor to retain sorted order in plot.
# Diverging Barcharts
ggplot(mtcars, aes(x=`car name`, y=mpg_z, label=mpg_z)) +
geom_bar(stat='identity', aes(fill=mpg_type), width=.5) +
scale_fill_manual(name="Mileage",
labels = c("Above Average", "Below Average"),
values = c("above"="#00ba38", "below"="#f8766d")) +
labs(subtitle="Normalised mileage from 'mtcars'",
title= "Diverging Bars") +
coord_flip()
# without any functions
ggplot(mpg, aes(x=class, y=hwy))+stat_summary()## No summary function supplied, defaulting to `mean_se()`
ggplot(mpg, aes(x=class, y=hwy))+stat_summary(fun=mean)## Warning: Removed 7 rows containing missing values (`geom_segment()`).
# with mean function and colored
ggplot(mpg, aes(x=class, y=hwy))+geom_point(alpha=.4,position="jitter")+stat_summary(fun=mean, color="red", size=1)## Warning: Removed 7 rows containing missing values (`geom_segment()`).
# with max value function and colored
ggplot(mpg, aes(x=class, y=hwy))+geom_point(alpha=.4,position="jitter")+stat_summary(fun=max, color="red", size=1)## Warning: Removed 7 rows containing missing values (`geom_segment()`).
# with min value function and colored
ggplot(mpg, aes(x=class, y=hwy))+geom_point(alpha=.4,position="jitter")+stat_summary(fun=min, color="red", size=1)## Warning: Removed 7 rows containing missing values (`geom_segment()`).
# with length value of each group and colored
ggplot(mpg, aes(x=class, y=hwy))+geom_point(alpha=.4,position="jitter")+stat_summary(fun=length, color="red", size=1)## Warning: Removed 7 rows containing missing values (`geom_segment()`).
# with mean value of each group and colored #### Very useful fun
ggplot(mpg, aes(x=class, y=hwy))+geom_point(alpha=.4,position="jitter")+geom_point(stat="summary", fun=mean, color="red", size=4)
#Count and hsitogram, single continuous datapoint
ggplot(mpg,aes(displ))+geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Count, max count and hsitogram between 0 to 1
ggplot(mpg,aes(displ))+geom_histogram(aes(y=stat(count/max(count))))## Warning: `stat(count / max(count))` was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count / max(count))` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# y aesthetic is automatically calculated
ggplot(diamonds, aes(x=price))+geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(diamonds, aes(x=price))+geom_bar(stat="bin")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# y aesthetic is automatically calculated
ggplot(diamonds, aes(x=price))+geom_histogram(bandwidth=200)## Warning in geom_histogram(bandwidth = 200): Ignoring unknown parameters:
## `bandwidth`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(diamonds, aes(x=price))+geom_histogram(bandwidth=50)## Warning in geom_histogram(bandwidth = 50): Ignoring unknown parameters:
## `bandwidth`## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(diamonds, aes(x=price))+geom_histogram(bins=10)
#Same as histogram but shows the data distribution like line...for categorical data
ggplot(diamonds, aes(x=price))+geom_freqpoly()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Line and histogram
ggplot(diamonds, aes(x=price))+geom_freqpoly()+geom_histogram(alpha=.4)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(diamonds, aes(x=price))+stat_bin(geom="line")+geom_freqpoly(color="red")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#for more smoothing
ggplot(diamonds, aes(x=price))+geom_freqpoly(binwidth=100)
#Histogram may not give clear insight
ggplot(diamonds, aes(x=price, fill=cut))+geom_histogram(binwidth=500)
# use freq_poly
ggplot(diamonds, aes(x=price, color=cut))+geom_freqpoly(binwidth=500)
#### Density Plots
ggplot(diamonds, aes(x=price))+geom_density()
ggplot(diamonds, aes(x=price, color=cut))+geom_density()
# Compare distributions using density_1
ggplot(diamonds, aes(x=price, color=cut, fill=cut))+geom_density(alpha=.4)
# Compare distributions using density_2 RigdelIne plots
#install package=ggridges
#have to specify the aes(y=)...
library(ggridges)
ggplot(diamonds, aes(x=depth, color=cut, fill=cut))+geom_density_ridges(aes(y=cut), alpha=.4)## Picking joint bandwidth of 0.193
#fair has minimum overlap## Box Plots
# they wont show whole distribution
ggplot(mpg, aes(x=class,y=hwy))+geom_boxplot()
ggplot(mpg, aes(x=class,y=hwy))+geom_boxplot(width=.4)
#to check how many points are there ex: 2seater has 5 data points
ggplot(mpg, aes(x=class,y=hwy))+geom_boxplot(varwidth=T)+geom_point(position="jitter", alpha=.3)
ggplot(mpg, aes(x=class,y=hwy))+geom_boxplot(aes(fill=class), show.legend=F)
#reverse orientation for a better look andvisibility
ggplot(mpg, aes(y=class,x=hwy))+geom_boxplot(aes(fill=class), show.legend=F)
library(viridis)
library(ggridges)
ggplot(mpg, aes(y=class,x=hwy))+geom_boxplot(aes(fill=class))+scale_fill_viridis(discrete=T)
# or
ggplot(mpg, aes(y=class,x=hwy))+geom_boxplot(aes(fill=class), show.legend=F,outlier.alpha=1)+scale_fill_viridis(discrete=T)
#Show mean with diff shape =square and in the box plot
ggplot(mpg, aes(y=class,x=hwy))+geom_boxplot(aes(fill=class), show.legend=F,outlier.alpha=1)+scale_fill_viridis(discrete=T)+geom_point(stat="summary", fun="mean", shape=15)
# with randmoness in the graph (avoid)
ggplot(mpg, aes(x=class,y=hwy))+geom_point(position=position_jitter())
#Position adjustments-To avoid the randomness(grapgh changes everytime) in jitter use seed=123/any
ggplot(mpg, aes(x=class,y=hwy))+geom_point(position=position_jitter(seed=123))
ggplot(mpg, aes(x=class,y=hwy,,color=class))+geom_point(show.legend=F,position=position_jitter(width=0.2,height=0))
x<-c(1:5)
y<-x
lab<-LETTERS[x]
df<-data.frame(x,y,lab)
df## x y lab
## 1 1 1 A
## 2 2 2 B
## 3 3 3 C
## 4 4 4 D
## 5 5 5 E#1 way
ggplot(df,aes(x,y))+geom_point(size=4,color="lightblue")+geom_text(aes(label=lab))
# 2nd way nudge it!
## check how much to nudge by Y=0.2/3/4...
# If you have negative values in the data then try y=-1/-2...etc-better
ggplot(df,aes(x,y))+geom_point(size=4,color="lightblue")+geom_text(aes(label=lab), position=position_nudge(y=0.45)) 
#3rd-way-
ggplot(df,aes(x,y))+geom_point(size=4,color="lightblue")+geom_text(aes(label=lab), nudge_x=0.1)
#Position Adjustment
# if you need less color then input alpha=0.5 in geom_bar
ggplot(mtcars,
aes(x=factor(cyl), fill=factor(vs)))+geom_bar()
#not that great
ggplot(mtcars,
aes(x=factor(cyl), fill=factor(vs)))+geom_bar(alpha=0.5, position="identity")
#Good
ggplot(mtcars,
aes(x=factor(cyl), fill=factor(vs)))+geom_bar(alpha=0.5, position="stack")
#Good
ggplot(mtcars,
aes(x=factor(cyl), fill=factor(vs)))+geom_bar(alpha=0.5, position="fill")
#Good-Reverse
ggplot(mtcars,
aes(x=factor(cyl), fill=factor(vs)))+geom_bar(alpha=0.5, position=position_fill(reverse=T))
#Density-overlapping
q<-ggplot(mtcars,aes(mpg,group=cyl, fill=cyl))
#Density-overlapping-easy to read
q+geom_density(alpha=0.6)
q+geom_density(position="identity",alpha=0.6)
# Stacked-Not easy to read
# Do not use position_stack & position_fill with Geom_line (scale will change and does not make any sense), only with geom_density
q+geom_density(position="stack",alpha=0.6)
#Postion dodge and position dodge2, use more position dodge2
p<-ggplot(mtcars,
aes(x=factor(cyl), fill=factor(vs)))
p+geom_bar(alpha=0.6)
p+geom_bar(position="dodge",alpha=0.6)
#for padding, dodge2
p+geom_bar(position="dodge2",alpha=0.6)
#increase the bar width (padding)
p+geom_bar(alpha=0.6,position=position_dodge(width=0.9))
#Better one
p+geom_bar(alpha=0.6,position=position_dodge(preserve="single"))
p+geom_bar(alpha=0.6,position=position_dodge2(preserve="single", reverse=T))
# increase/decrease the space between 2 bars i.e, padding
p+geom_bar(alpha=0.6,position=position_dodge2(preserve="single", reverse=T, padding=0.2))
# Extra-Histogram on a Categorical variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) +
theme(axis.text.x = element_text(angle=65, vjust=0.6)) +
labs(title="Histogram on Categorical Variable",
subtitle="Manufacturer across Vehicle Classes") 
g <- ggplot(mpg, aes(cty))
g + geom_density(aes(fill=factor(cyl)), alpha=0.8) +
labs(title="Density plot",
subtitle="City Mileage Grouped by Number of cylinders",
caption="Source: mpg",
x="City Mileage",
fill="# Cylinders")
# From on a categorical column variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) +
theme(axis.text.x = element_text(angle=65, vjust=0.6)) +
labs(title="Categorywise Bar Chart",
subtitle="Manufacturer of vehicles",
caption="Source: Manufacturers from 'mpg' dataset")
library(ggplot2)
theme_set(theme_classic())
# Allow Default X Axis Labels
ggplot(economics, aes(x=date)) +
geom_line(aes(y=pce)) +
labs(title="Time Series Chart",
subtitle="Returns Percentage from 'Economics' Dataset",
caption="Source: Economics",
y="Returns %")
# One Aesthetic , One Scale
# Aesthetics x=mpg, y=wt, color=cyl
# In this plot, x,y,color varibales has 3 scales by default
## scale_x_continuous,scale_y_continuous,scale_color_continuous
ggplot(mtcars,
aes(x=mpg, y=wt, color=cyl))+geom_point()+theme_classic()
#Geom_bar does the position adjustment by itself, meaning stacking done automatically
library(ggplot2)
ggplot(mtcars, aes(x=factor(cyl), fill=factor(vs)))+geom_bar()
ggplot(mtcars, aes(x=factor(cyl), fill=factor(mpg_type)))+geom_bar()
ggplot(mpg, aes(x=factor(cyl), fill=factor(class)))+geom_bar()
#use poition=identity. it does not makes sense to me to stack factor(cyl) on its own
ggplot(mtcars, aes(x=factor(cyl), fill=factor(vs)))+geom_bar(alpha=0.5, position="identity")
#This graph is normalised, in factor(cyl) in the category ofcylnder=4 , 90% have straight engine and 20% have V shaped engine
ggplot(mtcars, aes(x=factor(cyl), fill=factor(vs)))+geom_bar(alpha=0.5, position="fill")
ggplot(mtcars, aes(x=factor(cyl), fill=factor(vs)))+geom_bar(alpha=0.5, position=position_fill(reverse=TRUE))
#Overlap in the density graph
q <-ggplot(mtcars, aes(mpg, group=cyl, fill=cyl))
q+geom_density(position="identity",alpha=0.5)
## do not use position stack and position fill with ggplot
#position dodge
q <-ggplot(mtcars,aes(factor(cyl), fill=factor(vs)))
q+geom_bar(alpha=0.6,position="dodge") # dodged each bar w.o much space 
q+geom_bar(alpha=0.6,position=position_dodge(width=0.9))
q+geom_bar(alpha=0.6,position="dodge2") # dodged each bar with space 
q+geom_bar(alpha=0.6,position=position_dodge(preserve="single"))## best one, it preserves width consistancy
#only with dodge2, reverse and padding options (Position_dodge2 is an improved version of dodge so use this more)
q+geom_bar(alpha=0.6,position=position_dodge2(preserve="single", reverse=T)) # stack or bars swap
q+geom_bar(alpha=0.6,position=position_dodge2(preserve="single", reverse=T, padding=0.0)) # padding
#Scales: One aesthetic One scale
# here aesthetics: x=mpg [numeric], y=wt [numeric], color=cyl [numeric]
# here scales: x: scale_x_continuous, y: scale_y_continuous, color: scale_color_continuous
# Guides: horizonatal axis, vertical axis, colorbar
ggplot(mtcars,aes(x=mpg, y=wt, color=cyl))+geom_point()+theme_classic()
#test
ggplot(mpg,aes(x=displ, y=hwy, color=class))+geom_point()+theme_classic()
ggplot(mtcars,aes(x=mpg,y=wt, color=factor(cyl)))+geom_point()+theme_classic()+scale_x_continuous()+scale_y_continuous()+scale_color_discrete()
#changing the legend name
ggplot(mtcars,aes(x=mpg,y=wt,color=cyl))+geom_point()+theme_classic()+scale_color_continuous(name="Number of \ncyclinders")
#Binning
ggplot(mtcars,aes(x=mpg,y=wt,color=cyl))+geom_point()+theme_classic()+scale_color_binned()
# precedence Rule: The last Scale wins# Scales Classification
#Scale=continuous/discrete(factor)/binned(continuous data)
#by default it uses scale=continuous
ggplot(mtcars,aes(x=mpg,y=wt,color=cyl))+geom_point()
#Binned scale
ggplot(mtcars,aes(x=mpg,y=wt,color=cyl))+geom_point()+scale_color_binned()
#discrete
ggplot(mtcars,aes(x=mpg,y=wt,color=factor(cyl)))+geom_point()+scale_color_discrete()
# Scasles : Position
# Continuous data
p<-ggplot(mtcars,aes(x=mpg,y=wt))+geom_point(size=3)
p+scale_x_continuous(name="Miles per gallon")+scale_y_continuous(name="Weight of the Vehicle")
# add breaks
p+scale_x_continuous(name="Miles per gallon",breaks=c(10,20,30))
#add minor breaks
p+scale_x_continuous(name="Miles per gallon",breaks=c(10,20,30), minor_breaks=c(13,18,23,33))
# add lables to breaks
p+scale_x_continuous(name="Miles per gallon",n.breaks=6,labels=LETTERS[1:6])
# add limits
p+scale_x_continuous(name="Miles per gallon",limits=c(15,30))## Warning: Removed 9 rows containing missing values (`geom_point()`).
p+scale_x_continuous(name="Miles per gallon",limits=c(NA,22))## Warning: Removed 9 rows containing missing values (`geom_point()`).
# Transformation
p+scale_x_continuous(name="Miles per gallon",trans="log10")
p+scale_x_binned(nice.breaks=T)
#Discrete data set
q<-ggplot(mpg,aes(x=class,y=hwy))+geom_point(size=3)
q
q+scale_x_discrete(breaks=c("compact","pickup","suv")) # Specify the breaks
# specify the limits
q+scale_x_discrete(name="Class of vehicles", limits=c("compact","pickup","suv"))## Warning: Removed 92 rows containing missing values (`geom_point()`).
#abbreviate
q+scale_x_discrete(name="Class of vehicles", labels=abbreviate)
#Scales of Date or time scale
Sys.Date()## [1] "2024-05-27"x<-Sys.Date()-1:100
x## [1] "2024-05-26" "2024-05-25" "2024-05-24" "2024-05-23" "2024-05-22"
## [6] "2024-05-21" "2024-05-20" "2024-05-19" "2024-05-18" "2024-05-17"
## [11] "2024-05-16" "2024-05-15" "2024-05-14" "2024-05-13" "2024-05-12"
## [16] "2024-05-11" "2024-05-10" "2024-05-09" "2024-05-08" "2024-05-07"
## [21] "2024-05-06" "2024-05-05" "2024-05-04" "2024-05-03" "2024-05-02"
## [26] "2024-05-01" "2024-04-30" "2024-04-29" "2024-04-28" "2024-04-27"
## [31] "2024-04-26" "2024-04-25" "2024-04-24" "2024-04-23" "2024-04-22"
## [36] "2024-04-21" "2024-04-20" "2024-04-19" "2024-04-18" "2024-04-17"
## [41] "2024-04-16" "2024-04-15" "2024-04-14" "2024-04-13" "2024-04-12"
## [46] "2024-04-11" "2024-04-10" "2024-04-09" "2024-04-08" "2024-04-07"
## [51] "2024-04-06" "2024-04-05" "2024-04-04" "2024-04-03" "2024-04-02"
## [56] "2024-04-01" "2024-03-31" "2024-03-30" "2024-03-29" "2024-03-28"
## [61] "2024-03-27" "2024-03-26" "2024-03-25" "2024-03-24" "2024-03-23"
## [66] "2024-03-22" "2024-03-21" "2024-03-20" "2024-03-19" "2024-03-18"
## [71] "2024-03-17" "2024-03-16" "2024-03-15" "2024-03-14" "2024-03-13"
## [76] "2024-03-12" "2024-03-11" "2024-03-10" "2024-03-09" "2024-03-08"
## [81] "2024-03-07" "2024-03-06" "2024-03-05" "2024-03-04" "2024-03-03"
## [86] "2024-03-02" "2024-03-01" "2024-02-29" "2024-02-28" "2024-02-27"
## [91] "2024-02-26" "2024-02-25" "2024-02-24" "2024-02-23" "2024-02-22"
## [96] "2024-02-21" "2024-02-20" "2024-02-19" "2024-02-18" "2024-02-17"class(x)## [1] "Date"y<-runif(length(x))
df<-data.frame(x,y)
df## x y
## 1 2024-05-26 0.21080257
## 2 2024-05-25 0.06326395
## 3 2024-05-24 0.49294272
## 4 2024-05-23 0.75768409
## 5 2024-05-22 0.55442395
## 6 2024-05-21 0.80672002
## 7 2024-05-20 0.47193558
## 8 2024-05-19 0.33601824
## 9 2024-05-18 0.63270444
## 10 2024-05-17 0.62763058
## 11 2024-05-16 0.56467380
## 12 2024-05-15 0.89747894
## 13 2024-05-14 0.22439439
## 14 2024-05-13 0.65421490
## 15 2024-05-12 0.01103171
## 16 2024-05-11 0.17192668
## 17 2024-05-10 0.70427204
## 18 2024-05-09 0.26904693
## 19 2024-05-08 0.03091125
## 20 2024-05-07 0.86683527
## 21 2024-05-06 0.76042770
## 22 2024-05-05 0.38962511
## 23 2024-05-04 0.85200278
## 24 2024-05-03 0.14786717
## 25 2024-05-02 0.25017406
## 26 2024-05-01 0.49488590
## 27 2024-04-30 0.08010223
## 28 2024-04-29 0.46255211
## 29 2024-04-28 0.46946680
## 30 2024-04-27 0.27780700
## 31 2024-04-26 0.97154552
## 32 2024-04-25 0.85138281
## 33 2024-04-24 0.64458818
## 34 2024-04-23 0.06288825
## 35 2024-04-22 0.48353382
## 36 2024-04-21 0.34762297
## 37 2024-04-20 0.59756237
## 38 2024-04-19 0.11759753
## 39 2024-04-18 0.40029341
## 40 2024-04-17 0.28040995
## 41 2024-04-16 0.81973827
## 42 2024-04-15 0.41807390
## 43 2024-04-14 0.45422414
## 44 2024-04-13 0.99596343
## 45 2024-04-12 0.97252167
## 46 2024-04-11 0.73356425
## 47 2024-04-10 0.48287792
## 48 2024-04-09 0.05261873
## 49 2024-04-08 0.83534538
## 50 2024-04-07 0.70375295
## 51 2024-04-06 0.13709742
## 52 2024-04-05 0.33495806
## 53 2024-04-04 0.46789974
## 54 2024-04-03 0.11107006
## 55 2024-04-02 0.18462549
## 56 2024-04-01 0.67091625
## 57 2024-03-31 0.17603357
## 58 2024-03-30 0.48742834
## 59 2024-03-29 0.16469711
## 60 2024-03-28 0.33332635
## 61 2024-03-27 0.07533702
## 62 2024-03-26 0.38264830
## 63 2024-03-25 0.88508848
## 64 2024-03-24 0.38839207
## 65 2024-03-23 0.74335948
## 66 2024-03-22 0.81411670
## 67 2024-03-21 0.43131061
## 68 2024-03-20 0.03994631
## 69 2024-03-19 0.74461118
## 70 2024-03-18 0.01811422
## 71 2024-03-17 0.29668837
## 72 2024-03-16 0.40472144
## 73 2024-03-15 0.34839348
## 74 2024-03-14 0.08024099
## 75 2024-03-13 0.37202283
## 76 2024-03-12 0.85728041
## 77 2024-03-11 0.95764341
## 78 2024-03-10 0.66921097
## 79 2024-03-09 0.20795149
## 80 2024-03-08 0.16358109
## 81 2024-03-07 0.67820655
## 82 2024-03-06 0.35749835
## 83 2024-03-05 0.84962990
## 84 2024-03-04 0.17431397
## 85 2024-03-03 0.71817110
## 86 2024-03-02 0.48946851
## 87 2024-03-01 0.38199432
## 88 2024-02-29 0.02808988
## 89 2024-02-28 0.70252665
## 90 2024-02-27 0.18640196
## 91 2024-02-26 0.53917382
## 92 2024-02-25 0.29063522
## 93 2024-02-24 0.69803503
## 94 2024-02-23 0.67555989
## 95 2024-02-22 0.26169852
## 96 2024-02-21 0.12613478
## 97 2024-02-20 0.64598750
## 98 2024-02-19 0.96027557
## 99 2024-02-18 0.02882921
## 100 2024-02-17 0.31317013p<-ggplot(df,aes(x,y))+geom_line()
p
# or
p+scale_x_date()
p+scale_x_date(name="Date")
# specify the date breaks
MyBreaks<-seq(min(df$x),max(df$x), by=20)
MyBreaks## [1] "2024-02-17" "2024-03-08" "2024-03-28" "2024-04-17" "2024-05-07"p+scale_x_date(breaks=MyBreaks)
# Check only for 2 weeks
p+scale_x_date(date_breaks="4 weeks")
p+scale_x_date()
#Change the data name
p+scale_x_date(date_labels="%b-%d")
p+scale_x_date(date_breaks="1 month", date_labels=paste("Month","%m"))
# Scales: Color scales
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_color_continuous()
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_color_gradient()
# gradient = colors (low, high)
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_color_gradient(low="blue",high="red")
# gradient2 = colors (low,mid, high), by default mid value=0 in gradient2
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_color_gradient2(low="blue",mid="yellow",high="red")
# to get the right mid value
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_color_gradient2(low="blue",mid="yellow",high="green", midpoint=6.5)
# Scale color
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_color_steps(low="blue",high="red")
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_color_steps2(low="blue",mid="yellow",high="red", midpoint=6.5)
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_color_stepsn(colors=c("midnightblue","yellow","red","black"))
ggplot(mtcars, aes(x=mpg,y=wt,color=factor(cyl)))+geom_point(size=3)+scale_color_discrete()
#Colour: Viridis Family
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_colour_viridis_c()
#binning
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_colour_viridis_b()
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_colour_viridis_b(direction=1)
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_colour_viridis_b(direction=-1)
#Option=A or magma
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_colour_viridis_b(option="A") 
#Option=B or inferno
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_colour_viridis_b(option="inferno") 
#Option=C or Plasma
ggplot(mtcars, aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)+scale_colour_viridis_b(option="C") 
#discretizing the data
ggplot(mtcars, aes(x=mpg,y=wt,color=factor(cyl)))+geom_point(size=3)+scale_colour_viridis_d()
## Colorbrewer family# continuous: scale_colour_distiller/scale_filler_distiller
# binned: scale_colour_fermenter/scale_filler_fermenter
# discrete: scale_colour_brewer/scale_filler_brewer
p<-ggplot(mtcars,aes(x=mpg,y=wt,color=cyl))+geom_point(size=3)
p
p+scale_color_distiller()
p+scale_color_distiller(type="seq")
p+scale_color_distiller(type="seq", palette=1)
p+scale_color_distiller(type="seq", palette=2)
p+scale_color_distiller(type="seq", palette=3)
p+scale_color_distiller(type="div")
p+scale_color_distiller(type="div",palette=4)
p+scale_color_distiller(type="qual",palette=4)## Warning: Using a discrete colour palette in a continuous scale
## ℹ Consider using `type = "seq"` or `type = "div"` instead
p+scale_color_distiller(type="qual")## Warning: Using a discrete colour palette in a continuous scale
## ℹ Consider using `type = "seq"` or `type = "div"` instead
#scale_*_manual-check later
#Labs, Title
p+labs(x="x-test",y="y-test",title="Test to test", subtitle="Subtitle",caption="This is by ravi")
# Numbering of plots using tag
p+labs(x="x-test",y="y-test",title="Test to test", subtitle="Subtitle",caption="This is by ravi", tag=1)
#lims: simple manipulations of the ggplots
p+labs(x="x-test",y="y-test",title="Test to test", subtitle="Subtitle",caption="This is by ravi",tag="a)")+xlim(10,20)## Warning: Removed 14 rows containing missing values (`geom_point()`).
p+labs(x="x-test",y="y-test",title="Test to test", subtitle="Subtitle",caption="This is by ravi",tag="a)")+xlim(NA,30) # not modifying the lower limit## Warning: Removed 4 rows containing missing values (`geom_point()`).
# or use lims(x,y)
p+labs(x="x-test",y="y-test",title="Test to test", subtitle="Subtitle",caption="This is by ravi",tag="a)")+lims(x=c(20,35),y=c(3,4))## Warning: Removed 29 rows containing missing values (`geom_point()`).
# PLOTS-BarPlot##############
#-->relationship between a numeric and a categorical variable
#-->Each bar is one categorical variable
#-->Length of the bar shows the numerica variable
#-->Histogram: geom_histogram() = stat_bin(), counts and bins; requires x-continuous data
#-->barplot: geom_bar() = stat_count(), only counts; discrete & continuous x data
#-->
diam<-diamonds[1:10]
head(diam)## # A tibble: 6 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48ggplot(diam,aes(x=depth))+geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(diam,aes(x=depth))+geom_bar() # only counts
ggplot(diam,aes(x=cut))+geom_bar() # only counts
# geom_bar()
ggplot(diam,aes(x=cut, fill=clarity))+geom_bar()
#stacking
ggplot(diam,aes(x=cut, fill=clarity))+geom_bar(position="stack") # but we cnnot see how tolerant is the a stack with another w.r.t clarity orcannot compare each proprtion difefrence
# Normalises proportionality w.r.t each chunks or group but still not clear
ggplot(diam,aes(x=cut, fill=clarity))+geom_bar(position="fill")
#use faceting or dodging for more clarity
ggplot(diam,aes(x=cut, fill=clarity))+geom_bar(position="dodge2")
ggplot(diam,aes(x=cut, fill=clarity))+geom_bar(position=position_dodge(preserve="single"))
# VIMP
ggplot(diam,aes(x=cut, fill=clarity))+geom_bar()+facet_wrap(vars(clarity))
library(dplyr)
mpg1<-filter(mpg, displ>1.8,cyl>4)
mpg1## # A tibble: 153 × 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 2.8 1999 6 auto… f 16 26 p comp…
## 2 audi a4 2.8 1999 6 manu… f 18 26 p comp…
## 3 audi a4 3.1 2008 6 auto… f 18 27 p comp…
## 4 audi a4 quattro 2.8 1999 6 auto… 4 15 25 p comp…
## 5 audi a4 quattro 2.8 1999 6 manu… 4 17 25 p comp…
## 6 audi a4 quattro 3.1 2008 6 auto… 4 17 25 p comp…
## 7 audi a4 quattro 3.1 2008 6 manu… 4 15 25 p comp…
## 8 audi a6 quattro 2.8 1999 6 auto… 4 15 24 p mids…
## 9 audi a6 quattro 3.1 2008 6 auto… 4 17 25 p mids…
## 10 audi a6 quattro 4.2 2008 8 auto… 4 16 23 p mids…
## # ℹ 143 more rowsfirst<-ggplot(mpg1, aes(x=manufacturer,y=cty,fill=model))+geom_col()
#flip axis : interhcnage the axes
ggplot(mpg1, aes(y=manufacturer,x=cty,fill=model))+geom_col()
#order your bars as per the values
library(forcats)## Warning: package 'forcats' was built under R version 4.3.2class(mpg$manufacturer)## [1] "character"mpg$manufacturer<-factor(mpg$manufacturer)
class(mpg$manufacturer)## [1] "factor"mpg$manufacturer<-fct_reorder(mpg$manufacturer,mpg$hwy)
mpg$manufacturer## [1] audi audi audi audi audi audi
## [7] audi audi audi audi audi audi
## [13] audi audi audi audi audi audi
## [19] chevrolet chevrolet chevrolet chevrolet chevrolet chevrolet
## [25] chevrolet chevrolet chevrolet chevrolet chevrolet chevrolet
## [31] chevrolet chevrolet chevrolet chevrolet chevrolet chevrolet
## [37] chevrolet dodge dodge dodge dodge dodge
## [43] dodge dodge dodge dodge dodge dodge
## [49] dodge dodge dodge dodge dodge dodge
## [55] dodge dodge dodge dodge dodge dodge
## [61] dodge dodge dodge dodge dodge dodge
## [67] dodge dodge dodge dodge dodge dodge
## [73] dodge dodge ford ford ford ford
## [79] ford ford ford ford ford ford
## [85] ford ford ford ford ford ford
## [91] ford ford ford ford ford ford
## [97] ford ford ford honda honda honda
## [103] honda honda honda honda honda honda
## [109] hyundai hyundai hyundai hyundai hyundai hyundai
## [115] hyundai hyundai hyundai hyundai hyundai hyundai
## [121] hyundai hyundai jeep jeep jeep jeep
## [127] jeep jeep jeep jeep land rover land rover
## [133] land rover land rover lincoln lincoln lincoln mercury
## [139] mercury mercury mercury nissan nissan nissan
## [145] nissan nissan nissan nissan nissan nissan
## [151] nissan nissan nissan nissan pontiac pontiac
## [157] pontiac pontiac pontiac subaru subaru subaru
## [163] subaru subaru subaru subaru subaru subaru
## [169] subaru subaru subaru subaru subaru toyota
## [175] toyota toyota toyota toyota toyota toyota
## [181] toyota toyota toyota toyota toyota toyota
## [187] toyota toyota toyota toyota toyota toyota
## [193] toyota toyota toyota toyota toyota toyota
## [199] toyota toyota toyota toyota toyota toyota
## [205] toyota toyota toyota volkswagen volkswagen volkswagen
## [211] volkswagen volkswagen volkswagen volkswagen volkswagen volkswagen
## [217] volkswagen volkswagen volkswagen volkswagen volkswagen volkswagen
## [223] volkswagen volkswagen volkswagen volkswagen volkswagen volkswagen
## [229] volkswagen volkswagen volkswagen volkswagen volkswagen volkswagen
## 15 Levels: land rover dodge lincoln ford mercury jeep chevrolet audi ... hondaggplot(mpg1, aes(y=manufacturer,x=cty,fill=class))+geom_col()
#make bars thinner
library("viridis")
ggplot(mpg1, aes(y=manufacturer,x=cty,fill=cyl))+geom_col(width=0.9)+scale_fill_viridis_b(option="E",direction=-1)
# Coordinate systems
p<-ggplot(mtcars, aes(x=mpg, y=hp))+geom_point()+geom_smooth()
p## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
p+coord_cartesian()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
p+scale_x_continuous(limits=c(10,20))## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 14 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 14 rows containing missing values (`geom_point()`).
p+coord_cartesian(xlim=c(15,20))## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
p+coord_cartesian(expand=F)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
p+coord_cartesian(clip="off")## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
# Complete Themes
# theme_minimal(), theme_classic(), theme_linedraw(), theme_void()
library(ggplot2)
p<-ggplot(mtcars,aes(x=wt,y=mpg,color=factor(cyl)))+geom_point(size=3)
p
f<-p+facet_grid(cols=vars(factor(cyl)))
f
p+theme_gray()
f+theme_gray()
f+theme_bw()
# Modifying Theme components
p<-ggplot(mtcars,aes(x=wt,y=mpg,color=factor(cyl)))+geom_point(size=3)+labs(title="This is a Title")
p+theme(plot.title=element_text(family="Times"))## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
p+theme(plot.title=element_text(family="Times", face="bold",size=20,color="darkblue",hjust=0.5))## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
## not found in Windows font database
p+theme(plot.title=element_text(family="Times", face="bold",size=20,color="darkblue",hjust=0.5,margin=margin(0,0,20,0)))## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
p+theme(axis.line=element_line(color="gray40",size=1))## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
p+theme(axis.line=element_line(color="gray40",size=1, lineend="butt",arrow=arrow()))
# Facets
ggplot(iris, aes(x=Sepal.Length, y=Petal.Length))+ geom_point(aes(color=Species))+facet_wrap(vars(Species))